Break, pre-break, and remaining instructions for processing vectors

ABSTRACT

The described embodiments provide a system that sets elements in a result vector based on an input vector. During operation, the system determines a location of a key element within the input vector. Next, the system generates a result vector. When generating the result vector, the system sets one or more elements of the result vector based on the location of the key element in the input vector.

RELATED APPLICATIONS

This application hereby claims the benefit of U.S. provisionalapplication No. 61/089,251, attorney docket no. APL-P7038PRV1, entitled“Macroscalar Processor Architecture,” by inventor Jeffry E. Gonion,filed 15 Aug. 2008.

This application is related to: (1) pending application no. not yetassigned, attorney docket no. APL-P7038US1, entitled “Method andApparatus for Executing Program Code,” by inventors Jeffry E. Gonion andKeith E. Diefendorff, filed on 7 Apr. 2009; and (2) pending applicationno. not yet assigned, attorney docket no. APL-P7038US3, entitled “CheckHazard Instructions for Processing Vectors,” by inventors Jeffry E.Gonion and Keith E. Diefendorff, filed on 7 Apr. 2009.

This application is also related to: (1) pending application Ser. No.12/237,212, attorney docket no. APL-P6031US1, entitled “ConditionalData-Dependency Resolution in Vector Processors,” by inventors Jeffry E.Gonion and Keith E. Diefendorff, filed 24 Sep. 2008; (2) pendingapplication Ser. No. 12/237,196, attorney docket no. APL-P6031US2,entitled “Generating Stop Indicators Based on Conditional DataDependency in Vector Processors,” by inventors Jeffry E. Gonion andKeith E. Diefendorff, filed 24 Sep. 2008; and (3) pending applicationSer. No. 12/237,190, attorney docket no. APL-P6031US3, entitled“Generating Predicate Values Based on Conditional Data Dependency inVector Processors,” by inventors Jeffry E. Gonion and Keith E.Diefendorff, filed 24 Sep. 2008.

BACKGROUND

1. Field of the Invention

The described embodiments relate to techniques for improving theperformance of computer systems. More specifically, the describedembodiments relate to a break, pre-break, and remaining instruction forprocessing vectors in program code.

2. Related Art

Recent advances in processor design have led to the development of anumber of different processor architectures. For example, processordesigners have created superscalar processors that exploitinstruction-level parallelism (ILP), multi-core processors that exploitthread-level parallelism (TLP), and vector processors that exploitdata-level parallelism (DLP). Each of these processor architectures hasunique advantages and disadvantages which have either encouraged orhampered the widespread adoption of the architecture. For example,because ILP processors can often operate on existing program code thathas undergone only minor modifications, these processors have achievedwidespread adoption. However, TLP and DLP processors typically requireapplications to be manually re-coded to gain the benefit of theparallelism that they offer, a process that requires extensive effort.Consequently, TLP and DLP processors have not gained widespread adoptionfor general-purpose applications.

One significant issue which affects the adoption of DLP processors isthe vectorization of loops in program code. In a typical program, alarge portion of execution time is spent in loops. Unfortunately, manyof these loops have characteristics that render them unvectorizable inexisting DLP processors. Thus, the performance benefits gained fromattempting to vectorize program code can be limited.

One significant obstacle to vectorizing loops in program code isdependencies between iterations of the loop. For example, loop-carrieddata dependencies and memory-address aliasing are two such dependencies.These dependencies can be identified by a compiler during the compiler'sstatic analysis of program code, but they cannot be completely resolveduntil runtime data is available. Thus, because the compiler cannotconclusively determine that runtime dependencies will not beencountered, the compiler cannot vectorize the loop. Hence, becauseexisting systems require that the compiler determine the extent ofavailable parallelism during compilation, relatively little code can bevectorized.

SUMMARY

The described embodiments provide a system (e.g., computer system 100 inFIG. 1) that sets elements in a result vector based on an input vector.During operation, the system first receives an input vector thatincludes N elements. The system then determines a location of a keyelement within the input vector. Next, the system generates a resultvector. When generating the result vector, the system sets one or moreelements of the result vector based on the location of the key elementin the input vector.

In some embodiments, the key element is a left-most element in the inputvector that contains a true indicator. In some of these embodiments,when generating the result vector, the system writes true indicators inthe elements in the result vector that precede the location of the keyelement in the input vector, and writes false indicators in theremaining elements in the result vector. In alternative embodiments,when generating the result vector, the system writes true indicators inthe set of elements in the result vector that are at or precede thelocation of the key element in the input vector, and writes falseindicators in the remaining elements in the result vector.

In some embodiments, the key element is a right-most element in theinput vector that contains a true indicator. In some of theseembodiments, generating the result vector involves writing trueindicators in the set of elements in the result vector that follow thelocation of the key element in the input vector, and writing falseindicators in the remaining elements in the set of elements in theresult vector.

In some embodiments, when determining a location of the key element, thesystem starts from a left-most or a right-most element in the set ofelements in the input vector, evaluating a value in each element untilan element is encountered that contains a true indicator. In some ofthese embodiments, the system terminates the determination when anelement is found that contains a true indicator.

In some embodiments, the processor receives a predicate vector. In theseembodiments, determining the location of the key element involvesdetermining the location of the key element within a set of elements inthe input vector for which corresponding elements in the predicatevector contain “true” indicators.

In some embodiments, the processor receives an initial result vector. Inthese embodiments, generating a result vector involves updating theinitial result vector by setting the elements of the result vector forwhich corresponding elements in the predicate vector contain trueindicators to indicate the location of the key element in the inputvector. In addition, if predication is used, generating a result vectorinvolves leaving unchanged the value in each element of the initialresult vector for which the corresponding element in the predicatevector contains a false indicator. On the other hand, if zeroing isused, generating a result vector involves writing a false indicator ineach element of the initial result vector for which the correspondingelement in the predicate vector contains a false indicator. In theseembodiments, the processor returns the updated initial result vector asthe result vector.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 presents a block diagram of a computer system in accordance withthe described embodiments.

FIG. 2 presents an expanded view of a processor in accordance with thedescribed embodiments.

FIG. 3 presents an expanded view of a vector execution unit inaccordance with the described embodiments.

FIG. 4 presents an example of the parallelization of a loop in programcode accordance with the described embodiments.

FIG. 5 presents two tables illustrating operation using vectorized codein accordance with the described embodiments.

FIG. 6 presents vectorized program code in accordance with the describedembodiments.

FIG. 7 presents exemplary vectorized program code in accordance with thedescribed embodiments.

FIG. 8 presents a vectorized loop from program code with memory aliasingin accordance with the described embodiments.

FIG. 9 presents a vectorized loop from program code with memory aliasingin accordance with the described embodiments.

FIG. 10 presents a vectorized loop from program code with pseudoloop-carried dependencies in accordance with the described embodiments.

FIG. 11 presents a vectorized loop from program code with conditionalupdates in accordance with the described embodiments.

FIG. 12 presents a section of source code in accordance with thedescribed embodiments.

FIGS. 13A-13B present a vectorized loop from program code withconsolidation in accordance with the described embodiments.

FIG. 14 presents an example loop from program code and a dependencychart in accordance with the described embodiments.

FIG. 15 presents a vectorized loop from program code with mutualdependences in accordance with the described embodiments.

FIG. 16 presents a vectorized loop from program code with mutualdependences in accordance with the described embodiments.

FIG. 17 presents a vectorized loop from program code with summation inaccordance with the described embodiments.

FIGS. 18A-18B present a vectorized loop from program code with summationin accordance with the described embodiments.

FIGS. 19A-19B present a vectorized loop from program code that has beenvectorized both horizontally and vertically in accordance with thedescribed embodiments.

FIG. 20 presents a flowchart illustrating a process for executingprogram code in accordance with the described embodiments.

In the figures, like reference numerals refer to the same figureelements.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the disclosed embodiments, and is provided inthe context of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present embodiments. Thus, the system is notlimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features disclosed herein.

The data structures and code herein described are typically stored on acomputer-readable storage device, which may be any device or medium thatcan store code and/or data for use by a computer system (e.g., computersystem 100). The computer-readable storage device includes, but is notlimited to, volatile memory, non-volatile memory, magnetic and opticalstorage devices such as disk drives, magnetic tape, CDs (compact discs),DVDs (digital versatile discs or digital video discs), or other mediacapable of storing computer-readable media now known or later developed.Note that non-statutory media such as signals are not included in thecomputer-readable storage devices in these embodiments.

The methods and processes described in the detailed description sectioncan be embodied as code and/or data, which can be stored in acomputer-readable storage device as described above. When a computersystem reads and executes the code and/or data stored on thecomputer-readable storage device, the computer system performs themethods and processes embodied as data structures and code and storedwithin the computer-readable storage medium.

Furthermore, the methods and processes described below can be includedin hardware modules. For example, the hardware modules can include, butare not limited to, application-specific integrated circuit (ASIC)chips, field-programmable gate arrays (FPGAs), and otherprogrammable-logic devices now known or later developed. When thehardware modules are activated, the hardware modules perform the methodsand processes included within the hardware modules.

Terminology

Throughout the description, we use the following terminology. Theseterms may be generally known in the art, but are described below toclarify the subsequent descriptions.

The term “cycle” as used in this description refers to a quantum of timein which an operation happens. Although a cycle can be exactly one clockcycle, in some embodiments one “cycle” includes two or more clockcycles. Moreover, although one operation may be performed during a givencycle, that operation may include any number of sub-operations. Forexample, when referring to a vector execution unit performing anoperation “in a cycle,” this means that sufficient time has passed toenable the execution unit to have completed the described operation.

The term “vector-length agnostic” as used in this description indicatesthat an operation (i.e., instruction, etc.) can be executed usingvectors of any length, up to the limitations imposed by the supportinghardware. For example, assuming that the vector execution hardwaresupports 256-bit vectors that can include eight separate four-bytewords, a vector-length agnostic operation can operate on any number ofthe eight words in the vector.

The term “active element,” as used in this description to refer to oneor more elements of a vector, indicates elements that are operated onduring a given operation. Generally, the described embodiments enable avector execution unit to selectively perform parallel operations on oneor more available elements in a given vector in parallel. For example,an operation can be performed on only the first two of eight elements ofthe vector in parallel. In this case, the first two elements are “activeelements,” while the remaining six elements are “inactive elements.” Inthe described embodiments, one or more other vectors can be used todetermine which elements in a given operand vector are active (i.e., areto be operated on). For example, a “predicate vector” (described indetail below) can include “active” elements that are used to determinewhich elements in the operand vector to perform operations on.

Computer System

FIG. 1 presents a block diagram of a computer system 100 in accordancewith the described embodiments. Computer system 100 includes processor102, L2 cache 106, memory 108, and mass-storage device 110. Processor102 includes L1 cache 104.

Processor 102 can be a general-purpose processor that performscomputational operations. For example, processor 102 can be a centralprocessing unit (CPU) such as a microprocessor, a controller, anapplication-specific integrated circuit (ASIC), or a field-programmablegate array (FPGA). In the described embodiments, processor 102 has oneor more mechanisms for vector processing (i.e., vector execution units).Processor 102's vector execution unit is described in detail below.

Mass-storage device 110, memory 108, L2 cache 106, and L1 cache 104 arecomputer-readable storage devices that collectively form a memoryhierarchy that stores data and instructions for processor 102.Generally, mass-storage device 110 is a high-capacity, non-volatilememory, such as a disk drive or a large flash memory, with a largeaccess time, while L1 cache 104, L2 cache 106, and memory 108 aresmaller, faster semiconductor memories that store copies of frequentlyused data. Memory 108 is typically a dynamic random access memory (DRAM)structure that is larger than L1 cache 104 and L2 cache 106, whereas L1cache 104 and L2 cache 106 are typically comprised of smaller staticrandom access memories (SRAM). In some embodiments, L2 cache 106, memory108, and mass-storage device 110 are shared between one or moreprocessors in computer system 100. Such memory structures are well-knownin the art and are therefore not described in more detail.

In some embodiments, the devices in the memory hierarchy (i.e., L1 cache104, etc.) can access (i.e., read and/or write) multiple cache lines percycle. These embodiments enable more effective processing of memoryaccesses that occur based on a vector of pointers or array indices tonon-contiguous memory addresses.

Computer system 100 can be incorporated into many different types ofelectronic devices. For example, computer system 100 can be part of adesktop computer, a laptop computer, a server, a media player, anappliance, a cellular phone, a piece of testing equipment, a networkappliance, a personal digital assistant (PDA), a hybrid device (i.e., a“smart phone”) or another electronic device.

Although we use specific components to describe computer system 100, inalternative embodiments different components may be present in computersystem 100. For example, computer system 100 may not include some of thememory hierarchy (e.g., memory 108 and/or mass-storage device 110).Alternatively, computer system 100 may include video cards,video-capture devices, user-interface devices, network cards, opticaldrives, and/or other peripheral devices that are coupled to processor102 using a bus, a network, or another suitable communication channel.Computer system 100 may also include one or more additional processors,wherein the processors share some or all of L2 cache 106, memory 108,and mass-storage device 110.

Processor

FIG. 2 presents an expanded view of processor 102 in accordance with thedescribed embodiments. As is shown in FIG. 2, processor 102 includes L1cache 104, integer execution unit 202, floating-point execution unit206, and vector execution unit 204 (integer execution unit 202,floating-point execution unit 206, and vector execution unit 204 as agroup are interchangeably referred to as “the execution units”). Each ofthe execution units is used for performing computational operations,such as logical operations, mathematical operations, or bitwiseoperations for an associated type of operand. More specifically, integerexecution unit 202 is used for performing computational operations thatinvolve integer operands, floating-point execution unit 206 is used forperforming computational operations that involve floating-pointoperands, and vector execution unit 204 is used for performingcomputational operations that involve vector operands. Integer executionunits and floating-point execution units are generally known in the artand are not described in more detail.

In the described embodiments, vector execution unit 204 is asingle-instruction-multiple-data (SIMD) execution unit that performsoperations in parallel on some or all of the data elements that areincluded in vectors of operands. FIG. 3 presents an expanded view ofvector execution unit 204 in accordance with the described embodiments.As is shown in FIG. 3, vector execution unit 204 includes a vectorregister file 300 and an execution unit 302. Vector register file 300includes a set of vector registers that can hold operand vectors andresult vectors for execution unit 302. In some embodiments, there are 32vector registers in the vector register file, and each register includes128 bits. In alternative embodiments, there are different numbers ofvector registers and/or different numbers of bits per register.

Execution unit 302 retrieves operands from registers in vector registerfile 300 and executes vector instructions that cause execution unit 302to perform operations in parallel on some or all of the data elements inthe operand vector. For example, execution unit 302 can perform logicaloperations, mathematical operations, or bitwise operations on theelements in the vector. Execution unit 302 can perform one vectoroperation per cycle (although, as described above, the “cycle” mayinclude more than one cycle of execution unit 302's clock).

In the described embodiments, execution unit 302 supports vectors thathold N data elements (e.g., bytes, words, doublewords, etc.). In theseembodiments, execution unit 302 can perform operations on Nor fewer ofthe data elements in an operand vector in parallel. For example,assuming an embodiment where the vector is 256 bits in length, the dataelements being operated on are four-byte words, and the operation isadding a value to the data elements, these embodiments can add the valueto any number of the words in the vector.

In the described embodiments, execution unit 302 includes at least onecontrol signal that enables the dynamic limitation of the data elementsin an operand vector on which execution unit 302 operates. Specifically,depending on the state of the control signal, execution unit 302 may ormay not operate on all the data elements in the vector. For example,assuming an embodiment where the vector is 512 bits in length and thedata elements being operated on are four-byte words, the control signalcan be asserted to prevent operations from being performed on some orall of 16 data words in the operand vector. Note that “dynamically”limiting the data elements in the operand vector upon which operationsare performed can involve asserting the control signal separately foreach cycle at runtime.

In some embodiments, based on the values contained in a vector ofpredicates or one or more scalar predicates, execution unit 302 appliesvector operations to selected vector data elements only. In someembodiments, the remaining data elements in a result vector remainunaffected (which we call “predication”) or are forced to zero (which wecall “zeroing”). In some of these embodiments, the clocks for the dataelement processing subsystems (“lanes”) that are unused due topredication or zeroing in execution unit 302 can be gated, therebyreducing dynamic power consumption in execution unit 302.

The described embodiments are vector-length agnostic. Thus, a compileror programmer need not have explicit knowledge of the vector lengthsupported by the underlying hardware (e.g., vector execution unit 302).In these embodiments, a compiler generates or a programmer writesprogram code that need not rely on (or use) a specific vector length(some embodiments are forbidden from even specifying a specific vectorsize in program code). Thus, the compiled code in these embodiments(i.e., binary code) runs on other embodiments with differing vectorlengths, while potentially realizing performance gains from processorsthat support longer vectors. Consequently, as process technology allowslonger vectors, legacy binary code simply speeds up without any effortby software developers.

In some embodiments, vector lengths need not be powers of two.Specifically, vectors of 3, 7, or another number of data elements can beused in the same way as vectors with power-of-two numbers of dataelements.

In the described embodiments, each data element in the vector cancontain an address that is used by execution unit 302 for performing aset of memory accesses in parallel. In these embodiments, if one or moreelements of the vector contain invalid memory addresses, invalidmemory-read operations can occur. In these embodiments, invalidmemory-read operations that would otherwise result in programtermination instead cause any elements with valid addresses to be readand elements with invalid elements to be flagged, allowing programexecution to continue in the face of speculative, and in hindsightillegal, read operations.

In some embodiments, processor 102 (and hence execution unit 302) isable to operate on and use vectors of pointers. In these embodiments,the number of data elements per vector is the same as the number ofpointers per vector, regardless of the size of the data type.Instructions that operate on memory may have variants that indicate thesize of the memory access, but elements in processor registers should bethe same as the pointer size. In these embodiments, processors thatsupport both 32-bit and 64-bit addressing modes may choose to allowtwice as many elements per vector in 32-bit mode, thereby achievinggreater throughput. This implies a distinct throughput advantage to32-bit addressing, assuming the same width data path.Implementation-specific techniques can be used to relax the requirement.For example, double-precision floating-point numbers can be supported in32-bit mode through register pairing or some other specializedmechanism.

Although we describe processor 102 as including a particular set ofexecution units, in alternative embodiments, processor 102 can includedifferent numbers or types of execution units. Moreover, although theembodiment shown in FIG. 2 is limited to a particular set of functionalblocks, in the described embodiments processor 102 can include otherfunctional blocks, such as an instruction fetch unit, an instructiondecode unit, a branch unit, a memory management unit, I/O interfaces,etc. coupled to the execution units. The additional functional blocksthat can be present in processor 102 are well-known in the art and arenot described in more detail.

Macroscalar Architecture

The described embodiments provide an instruction set and supportinghardware that allow compilers to generate program code for loops withouthaving to completely determine parallelism at compile-time, and withoutdiscarding useful static analysis information. Specifically, theseembodiments provide a set of instructions that do not mandateparallelism for loops but, instead, enable parallelism to be exploitedat runtime if dynamic conditions permit. These embodiments thus includeinstructions that enable code generated by the compiler to dynamicallyswitch between non-parallel (scalar) and parallel (vector) execution forloop iterations depending on conditions at runtime by switching theamount of parallelism used.

As described, these embodiments provide instructions that enable anundetermined amount of vector parallelism for loop iterations but do notrequire that the parallelism be used at runtime. More specifically,these embodiments include a set of vector-length agnostic instructionswhose effective vector length can vary depending on runtime conditions.Thus, if runtime dependencies demand non-parallel execution of the code,then execution occurs with an effective vector length of one element.Likewise, if runtime conditions permit parallel execution, the same codeexecutes in a vector-parallel manner to whatever degree is allowed byruntime dependencies (and the vector length of the underlying hardware).For example, if two out of eight elements of the vector can safelyexecute in parallel, the described embodiments execute the two elementsin parallel. In these embodiments, expressing program code in avector-length agnostic format enables a broad range of vectorizationopportunities that are not present in existing systems.

In the described embodiments, during compilation, a compiler firstanalyzes the loop structure of a given loop in program code and performsstatic dependency analysis. The compiler then generates program codethat retains static analysis information and instructs processor 102 howto resolve runtime dependencies and process the program code with themaximum amount of parallelism possible. More specifically, the compilerprovides vector instructions for performing corresponding sets of loopiterations in parallel, and provides vector-control instructions fordynamically limiting the execution of the vector instructions to preventdata dependencies between the iterations of the loop from causing anerror. This approach defers the determination of parallelism to runtime,where the information on runtime dependencies is available, therebyallowing the software and processor to adapt parallelism to dynamicallychanging conditions.

FIG. 4 presents an example of the parallelization of a loop in programcode in accordance with the described embodiments. On the left side ofFIG. 4 is shown an execution pattern for four iterations of a loop thathave not been parallelized, where each loop includes instructions A-G.On the right side of FIG. 4 is shown a parallelized version of the loopin accordance with the described embodiments. In this example, we assumethat each instruction within an iteration depends on at least oneinstruction before it, so that there is a static dependency chainbetween the instructions of a given iteration. Hence, the instructionswithin a given iteration cannot be parallelized (i.e., instructions A-Gwithin a given iteration are always serially executed with respect tothe other instructions in the iteration). Note that in alternativeembodiments the instructions within a given iteration can beparallelizable.

As shown by the arrows between the iterations of the loop in FIG. 4,there is a possibility of a runtime data dependency between instructionE in a given iteration and instruction D of the subsequent iteration.However, during compilation, the compiler can only determine that thepossibility of data dependency exists between these instructions. Thecompiler cannot tell in which iterations dependencies will actuallymaterialize, because this information is only available at runtime. Inthis example, a data dependency that actually materializes at runtime isshown by the solid arrows in FIG. 4, while a data dependency thatdoesn't materialize at runtime is shown using a dashed arrow. Thus, asshown in FIG. 4, a runtime data dependency actually occurs between thefirst/second and third/fourth iterations.

Because no data dependency exists between the second and thirditerations, the second and third iterations can safely be processed inparallel. Furthermore, instructions A-C and F-G of a given iterationhave dependencies only within an iteration and, therefore, instruction Aof a given iteration is able to execute in parallel with instruction Aof any other iteration, instruction B can also execute in parallel withinstruction B of any other iteration, and so forth.

The right side of FIG. 4 shows a version of the loop that has beenparallelized in accordance with the observations above. The exampleshows how the iterations of such a loop can be executed to accommodateboth the static and runtime data dependencies, while achieving maximumparallelism. For instructions A-C, all four iterations can be executedin parallel. Then instructions D and E can be executed with the maximumamount of parallelism allowed by the runtime data dependencies.Specifically, because instruction D in the second iteration depends oninstruction E in the first iteration, instructions D and E in the firstiteration must be executed before instruction D for the second iterationcan be executed. However, because there is no data dependency betweenthe second and third iterations, instructions D and E for theseiterations can be executed in parallel. Finally, for instructions F-G,all four iterations can be executed in parallel.

EXAMPLES OF THE EMBODIMENTS

In the following section, we present a series of examples in describingthe embodiments. These examples introduce Macroscalar operations anddemonstrate their use in vectorizing loops in accordance with thedescribed embodiments. For ease of understanding, these examples arepresented using C++-formatted pseudocode.

The instructions and operations presented in this description areintended to aid in understanding the described embodiments. However, inalternative embodiments, an instruction or operation can be implementedin a different way, for example, using a microcode sequence of moreprimitive operations or using a different sequence of sub-operations.Note that further decomposition of instructions is avoided so thatinformation about the macro-operation and the corresponding usage modelis not obscured. Note also that additional definitions for eachinstruction may be provided in the “Macroscalar Instructions” section inthis description.

Notation

In describing the examples, we use the following formats for variables,which are vector quantities unless otherwise noted:

p5=a<b;

-   -   Elements of vector p5 are set to 0 or 1 depending on the result        of testing a <b. (Note that vector p5 can be a “predicate        vector,” as described in detail below.)

˜p5; a=b+c;

-   -   Only elements in vector a designated by active (i.e., non-zero)        elements in the predicate vector p5 receive the result of b+c.        The remaining elements of a are unchanged. This operation is        called “predication,” and is denoted using the tilde (“˜”) sign        before the predicate vector.

!p5; a=b+c;

-   -   Only elements in vector a designated by active (i.e., non-zero)        elements in the predicate vector p5 receive the result of b+c.        The remaining elements of a are set to zero. This operation is        called “zeroing,” and is denoted using the exclamation point        (“!”) sign before the predicate vector.

if (FIRST(p1)) ...; (also LAST(p1), ANY(p1), ALL(p1), or NONE(p1))

-   -   If the first element (or the last, any, all, or none) of p1 is        non-zero, perform the ( . . . ) action. Note that in the C++        model, the functions FIRST( ), LAST( ), ANY( ), ALL( ), and        NONE( ) return an integer value, and use of the “!” operator, as        in “if (!LAST(p1)),” is also common. In such a context the “!”        unary operator retains the conventional C-language meaning of        “not,” and does not imply a zeroing predicate.

~p0; if (FIRST(p1)) ...; (also LAST(p1), ANY(p1), ALL(p1), or NONE(p1)

Within the set of elements indicated by active elements in predicate p0,if the first element (or the last, any, all, or none) of p1 is non-zero,perform the ( . . . ) action.

x+=VECLEN;

-   -   VECLEN is a machine value that communicates the number of        elements per vector. The value is determined at runtime by the        processor executing the code, rather than being determined by        the assembler.

//Comment

-   -   In a similar way to many common programming languages, the        following examples use the double forward slash to indicate        comments. These comments can provide information regarding the        values contained in the indicated vector or explanation of        operations being performed in a corresponding example.

In these examples, other C++-formatted operators retain theirconventional meanings, but are applied across the vector on anelement-by-element basis. Where function calls are employed, they implya single instruction that places any value returned into a destinationregister. For simplicity in understanding, all vectors discussed in thispaper are vectors of integers, but alternative embodiments support otherdata formats.

Structural Loop-Carried Dependencies

Example 1 presents an example loop in program code that is“non-vectorizable” using conventional vector architectures. (Note thatin addition to being non-vectorizable, this loop is also notmulti-threadable on conventional multi-threading architectures due tothe fine-grain nature of the data dependencies.) For clarity, this loophas been distilled to the fundamental loop-carried dependencies thatmake the loop unvectorizable.

In this example, the variables r and s have loop-carried dependenciesthat prevent vectorization using conventional architectures. Notice,however, that the loop is vectorizable as long as the condition(A[x]<FACTOR) is known to be always true or always false. Theseassumptions change when the condition is allowed to vary duringexecution (the common case). For simplicity in this example, we presumethat no aliasing exists between A[ ] and B[ ]. Note that aliasing isaddressed in later sections.

r = 0; s = 0; for (x=0; x<KSIZE; ++x) {    if (A[x] < FACTOR)    {      r = A[x+s];    }    else    {       s = A[x+r];    }    B[x] = r +s; }

Example 1 Program Code Loop

In the described embodiments, the loop in Example 1 can be vectorized bypartitioning the vector into segments for which the conditional(A[x]<FACTOR) does not change. Exemplary processes for partitioning suchvectors, as well as exemplary instructions that enable the partitioning,are presented below. Note that for this example the describedpartitioning need only be applied to instructions within the conditionalclause. The first read of A[x] and the final operation B[x]=r+s canalways be executed in parallel across a full vector, except potentiallyon the final loop iteration.

We now present instructions and exemplary vectorized code in order toexplain the described embodiments. The following description isgenerally organized so that a number of instructions are described andthen one or more vectorized code samples that use the instructions arepresented. In some cases, a particular type of vectorization issue isexplored in a given example.

dest=VectorReadInt(base,offset)

VectorReadInt is an instruction for performing a memory read operation.A vector of offsets, offset, scaled by the data size (integer in thiscase) is added to a scalar base address, base, to form a vector ofmemory addresses which are then read into a destination vector. If theinstruction is predicated or zeroed, only addresses corresponding toactive elements are read. In the described embodiments, reads to invalidaddresses are allowed to fault, but such faults only result in programtermination if the first active address is invalid.

VectorWriteInt(base, offset, value)

VectorWriteInt is an instruction for performing a memory writeoperation. A vector of offsets, offset, scaled by the data size (integerin this case) is added to a scalar base address, base, to form a vectorof memory addresses. A vector of values, value, is written to thesememory addresses. If this instruction is predicated or zeroed, data iswritten only to active addresses. In the described embodiments, writesto illegal addresses always generate faults.

dest=VectorIndex(start,increment)

VectorIndex is an instruction for generating vectors of values thatmonotonically adjust by the increment from a scalar starting valuespecified by start. This instruction can be used for initializing loopindex variables when the index adjustment is constant. When predicationor zeroing is applied, the first active element receives the startingvalue, and the increment is only applied to subsequent active elements.For example:

x=VectorIndex(0,1); //x={0 1 2 3 4 5 6 7}

dest=PropagatePostT(dest,src,pred)

The PropagatePostT instruction propagates the value of active elementsin src, as determined by pred, to subsequent inactive elements of dest.Active elements, and any inactive elements that precede the first activeelement, remain unchanged in dest. The purpose of this instruction is totake a value that is conditionally calculated, and propagate theconditionally calculated value to subsequent loop iterations as occursin the equivalent scalar code. For example:

Entry: dest  = { 8 9 A B C D E F } src   = { 1 2 3 4 5 6 7 8 } pred = {0 0 1 1 0 0 1 0 } Exit: dest  = { 8 9 A B 4 4 E 7 }

dest=PropagatePriorF(src,pred)

The PropagatePriorF instruction propagates the value of the inactiveelements of src, as determined by pred, into subsequent active elementsin dest. Inactive elements are copied from src to dest. If the firstelement of the predicate is active, then the last element of src ispropagated to that position. For example:

Entry: src   = { 1 2 3 4 5 6 7 8 } pred = { 1 0 1 1 0 0 1 0 } Exit: dest = { 8 2 2 2 5 6 6 8 }

dest=ConditionalStop(pred,deps)

The ConditionalStop instruction evaluates a vector of predicates, pred,and identifies transitions between adjacent predicate elements thatimply data dependencies as specified by deps. The scalar value deps canbe thought of as an array of four bits, each of which designates apossible transition between true/false elements in pred, as processedfrom left to right. These bits convey the presence of the indicateddependency if set, and guarantee the absence of the dependency if notset. They are:

-   -   kTF—Implies a loop-carried dependency from an iteration for        which the predicate is true, to the subsequent iteration for        which the value of the predicate is false.    -   kFF—Implies a loop-carried dependency from an iteration for        which the predicate is false, to the subsequent iteration for        which the value of the predicate is false.    -   kFT—Implies a loop-carried dependency from an iteration for        which the predicate is false, to the subsequent iteration for        which the value of the predicate is true.    -   kTT—Implies a loop-carried dependency from an iteration for        which the predicate is true, to the subsequent iteration for        which the value of the predicate is true.

The element position corresponding to the iteration that generates thedata that is depended upon is stored in the destination vector at theelement position corresponding to the iteration that depends on thedata. If no data dependency exists, a value of 0 is stored in thedestination vector at that element. The resulting dependency indexvector, or DIV, contains a vector of element-position indices thatrepresent dependencies. For the reasons described below, the firstelement of the vector is element number 1 (rather than 0).

As an example, consider the dependencies in the loop of Example 1. Inthis loop, transitions between true and false iterations of theconditional clause represent a loop-carried dependency that requires abreak in parallelism. This can be handled using the followinginstructions:

p1 = (t < FACTOR); // p1 = { 0 0 0 0 1 1 0 0 } p2 = ConditionalStop(p1,kTF|kFT); // p2 = { 0 0 0 0 4 0 6 0 }

Because the 4th iteration generates the required data, and the 5thiteration depends on it, a 4 is stored in position 5 of the outputvector p2 (interchangeably called the “dependency index vector” or“DIV”). The same applies for the 7th iteration, which depends on datafrom the 6th iteration. Other elements of the DIV are set to 0 toindicate the absence of dependencies. (Note that in this example thefirst element of the vector is element number 1.)

dest=GeneratePredicates(pred,DIV)

GeneratePredicates takes the dependency index vector, DIV, and generatespredicates corresponding to the next group of elements that may safelybe processed in parallel, given the previous group that was processed,indicated by pred. If no elements of pred are active, predicates aregenerated for the first group of elements that may safely be processedin parallel. If pred indicates that the final elements of the vectorhave been processed, then the instruction generates a result vector ofinactive predicates indicating that no elements should be processed.Using the values in the first example, GeneratePredicates operates asfollows:

Entry Conditions: // i2 = { 0 0 0 0 4 0 6 0 } p2 = 0; // p2 = { 0 0 0 00 0 0 0 } Loop2:    p2 = GeneratePredicates(p2,i2); // p2′ = { 1 1 1   1 0 0 0 0 }    if (!LAST(p2)) goto Loop2; // p2″ = { 0 0 0 0 1    1 00 }                 // p2′″ = { 0 0 0 0    0 0 1 1 }

From an initialized predicate p2 of all zeros, GeneratePredicatesgenerates new instances of p2 that partition subsequent vectorcalculations into three sub-vectors (i.e., p′, p″, and p′″). Thisenables the hardware to process the vector in groups that avoidviolating the data dependencies of the loop.

FIG. 5 presents two tables illustrating operation using vectorized codein accordance with the described embodiments. The top of FIG. 5 presentsa table illustrating a sequence of states through which scalar executionof the loop in Example 1 might progress using a randomized 50/50distribution of the direction of the conditional expression. The bottomof FIG. 5 presents a table illustrating a progression of execution forMacroscalar vectorized program code in accordance with the describedembodiments. In FIG. 5, the values read from A[ ] are shown usingleftward-slanting hash marks, while the values written to B[ ] are shownusing rightward-slanting hash marks, and values for “r” or “s”(depending on which is changed in a given iteration) are shown using ashaded background. Observe that “r” never changes while “s” is changing,and vice-versa.

Nothing prevents all values from being read from A[ ] in parallel orwritten to B[ ] in parallel, because neither set of values participatesin the loop-carried dependency chain. However, for the calculation of rand s, elements can be processed in parallel only while the value of theconditional expression remains the same (i.e., runs of true or false).This pattern for the execution of the program code for this loop isshown in the lower portion of FIG. 5. Note that the example uses vectorseight elements in length. When processing the first vector instruction,the first iteration is performed alone (i.e., execution unit 302processes only the first vector element), whereas iterations 1-5 areprocessed in parallel by execution unit 302, and then iterations 6-7 areprocessed in parallel by execution unit 302.

FIG. 6 presents exemplary vectorized program code in accordance with thedescribed embodiments. In FIG. 6, the top portion contains the originalsource code, while the bottom portion contains vectorized coderepresenting the operations that may be performed by the describedembodiments. In the vectorized code, Loop1 is the loop from the sourcecode, while Loop2 is the vector-partitioning loop that processes thesub-vector partitions.

In the example, array A[ ] is read and compared in full-length vectors(i.e., for a vector of N elements, N positions of array A[ ] are read atonce). Vector i2 is the DIV that controls partitioning of the vector.Partitioning is determined by monitoring the predicate p1 fortransitions between false and true, which indicate loop-carrieddependencies that should be observed. Predicate vector p2 determineswhich elements are to be acted upon at any time. In this particularloop, p1 has the same value in all elements of any sub-vector partition;therefore, only the first element of the partition needs to be checkedto determine which variable to update.

After variable s is updated, the PropagatePostT instruction propagatesthe final value in the active partition to subsequent elements in thevector. At the top of the loop, the PropagatePriorF instruction copiesthe last value of s from the final vector position across all elementsof the vector in preparation for the next pass. Note that variable r ispropagated using a different method, illustrating the efficiencies ofusing the PropagatePriorF instruction in certain cases.

Software Speculation

In the previous example, the vector partitions prior to the beginning ofthe vector-partitioning loop could be determined because thecontrol-flow decision was independent of the loop-carried dependencies.This is not always the case. Consider the following two loops:

j = 0; for (x=0; x<KSIZE; ++x) {    if (A[x] < FACTOR)    {       j =A[x+j];    }    B[x] = j; }

Example 2A Program Code Loop

j = 0; for (x=0; x<KSIZE; ++x) {    if (A[x+j] < FACTOR)    {       j =A[x];    }    B[x] = j; }

Example 2B Program Code Loop

In Example 2A, the control-flow decision is independent of theloop-carried dependency chain, while in Example 2B the control flowdecision is part of the loop-carried dependency chain. In someembodiments, the loop in Example 2B leads the described embodiments tospeculate that the value of j will remain unchanged and compensate laterif this prediction proves incorrect. In these embodiments, thespeculation on the value of j does not significantly change thevectorization of the loop.

In some embodiments, the compiler can be configured to always predict nodata dependencies between the iterations of the loop. In theseembodiments, in the case that runtime data dependencies exist, the groupof active elements processed in parallel can be reduced to represent thegroup of elements that may safely be processed in parallel at that time.In these embodiments, there is little penalty for mispredicting moreparallelism than actually exists because no parallelism is actually lost(i.e., if necessary, the iterations can be processed one element at atime, in a non-parallel way). In these embodiments, the actual amount ofparallelism is simply recognized at a later stage.

dest=VectorReadIntNF(base,offset)

VectorReadIntNF is a non-faulting variant of VectorReadInt. Thisinstruction does not generate a fault if at least the first activeelement is a valid address. Results corresponding to invalid addressesare forced to zero, and flags are returned that can be used to maskpredicates to later instructions that use this data. If the first activeelement of the address is unmapped, this instruction faults to allow thevirtual memory system to populate a corresponding page, thereby ensuringthat processor 102 can continue to make forward progress.

dest=Remaining(pred)

The Remaining instruction evaluates a vector of predicates, pred, andcalculates the remaining elements in the vector. This corresponds to theset of inactive predicates following the last active predicate. If thereare no active elements in pred, a vector of all active predicates isreturned. Likewise, if pred is a vector of all active predicates, avector of inactive predicates is returned. For example:

Entry: pred = { 0 0 1 0 1 0 0 0 } Exit: dest  = { 0 0 0 0 0 1 1 1 }

FIG. 7 presents exemplary vectorized code in accordance with thedescribed embodiments. As shown in FIG. 7, the top code sample is avectorized version of the code in Example 2A (as presented above), andthe bottom code sample is a vectorized version of the code in Example2B. In the vectorized code for Example 2B, the read of A[ ] andsubsequent comparison have been moved inside the vector-partitioningloop. Thus, these operations presume (speculate) that the value of jdoes not change. Only after using j is it possible to determine where jmay change value. After j is updated, the remaining vector elements arere-computed as necessary to iterate through the entire vector. The useof the Remaining instruction in the speculative code sample allows theprogram to determine which elements remain to be processed in thevector-partitioning loop before the program can determine the sub-groupof these elements that are actually safe to process (i.e., that don'thave unresolved data dependencies).

The described embodiments include fault-tolerant read support. Thus, inthese embodiments, processor 102 can speculatively read data from memoryusing addresses from invalid elements of a vector instruction (e.g,VectorReadNF) in an attempt to load values that are to be later used incalculations. However, upon discovering that an invalid read hasoccurred, these values are ultimately discarded and, therefore, notgermane to correct program behavior. Because such reads may referencenon-existent or protected memory, these embodiments are configured tocontinue normal execution in the presence of invalid but irrelevant datamistakenly read from memory. (Note that in embodiments that supportvirtual memory, this has the additional benefit of not paging until theneed to do so is certain.)

In the loop shown in FIG. 7, there exists a loop-carried dependencybetween iterations where the condition is true, and subsequentiterations, regardless of the predicate value for the later iterations.This is reflected in the parameters of the ConditionalStop instruction.

The sample code in FIG. 7 highlights the differences betweennon-speculative and speculative vector partitioning. In Example 2A,memory is read and the predicate is calculated prior to theConditionalStop. The partitioning loop begins after the ConditionalStopinstruction. In Example 2B, the ConditionalStop instruction is executedinside the partitioning loop, and serves to recognize the dependenciesthat render earlier operations invalid. In both cases, theGeneratePredicates instruction calculates the predicates that controlwhich elements are used for the remainder of the partitioning loop.

Memory-Based Loop-Carried Dependencies

The examples presented thus far presume the compiler was able toestablish that no address aliasing existed at the time of compilation.Such determinations are often very difficult or impossible to make. Thenext example shows how loop-carried dependencies occurring throughmemory (which may include aliasing) are dealt with in the describedembodiments (i.e., in the Macroscalar architecture). Consider thefollowing loop:

for (x=0; x<KSIZE; ++x) {    r = C[x];    s = D[x];    A[x] = A[r] +A[s]; }

Example 3 Program Code Loop

In Example 3, the compiler cannot determine at compile-time whether A[x]aliases with A[r] or A[s]. However, in the described embodiments, thecompiler inserts instructions that cause the hardware to check formemory hazards at runtime and partitions the vector accordingly atruntime to ensure correct program behavior.

dest=CheckHazardP(first,second,pred)

CheckHazardP examines two vectors of memory addresses (or indices)corresponding to two memory operations for potential data dependenciesthrough memory. The vector first holds addresses for the first memoryoperation, and vector second holds addresses for the second operation.The predicate pred indicates which elements of second are to be operatedupon. As scalar loop iterations proceed forward in time, vector elementsrepresenting sequential iterations appear left to right within vectors.CheckHazardP evaluates hazards in this context. The instructioncalculates a DIV representing memory hazards between the correspondingpair of first and second memory operations. The instruction correctlyevaluates write-after-read, read-after-write, and write-after-writememory hazards.

As with the ConditionalStop instruction, the element positioncorresponding to the iteration that generates the data that is dependedupon is stored in the destination vector at the element positioncorresponding to the iteration that is dependent upon the data. If nodata dependency exists, a zero is stored in the destination vector atthe element position corresponding to the iteration that does not havethe dependency. For example:

Entry: first = { 2 3 4 5 6 7 8 9 } second = { 8 7 6 5 4 3 2 1 } pred = {1 1 1 1 1 1 1 1 } Exit: dest = { 0 0 0 0 3 2 1 0 }

Here, element 5 of the first vector (“first”) and element 3 of thesecond vector (“second”) both access array index 6. Therefore, a 3 isstored in position 5 of DIV. Likewise, element 6 of first and element 2of second both access array index position 7, causing a 2 to be storedin position 6 of DIV, and so forth. A zero is stored in DIV where nodata dependencies exist.

In some embodiments, the CheckHazardP instruction is configured toaccount for various sizes of data types. However, for clarity wedescribe the function of the instruction using only array index types.

The example above has three memory hazards. However, in the describedembodiments, only two partitions are needed to safely process theassociated memory operations. Close inspection reveals that handling thefirst hazard on element position 3 renders subsequent dependencies onlower or equally numbered element positions moot. For example:

Entry Conditions: // DIV = { 0 0 0 0 3 2 1 0 } // p2 = { 0 0 0 0 0 0 0 0} p2 = GeneratePredicates(p2,DIV); // p2 = { 1 1 1 1 0 0 0 0 } p2 =GeneratePredicates(p2,DIV); // p2 = { 0 0 0 0 1 1 1 1 }

The process used by the described embodiments to analyze a DIV todetermine where a vector should be broken is shown in pseudocode below.In some embodiments, processor 102 performs this calculation inparallel. For example:

List = <empty>; for (x=STARTPOS; x<VECLEN; ++x)    if (DIV[x] in List)      Break from loop;    else if (DIV[x] > 0)       Append <x> to List;

The vector may safely be processed in parallel over the interval[STARTPOS, x), where x is the position where DIV[x]>0, that is, fromSTARTPOS up to (but not including) position x, where STARTPOS refers tothe first vector element after the set of elements previously processed.If the set of previously processed elements is empty, then STARTPOSbegins at the first element.

In some embodiments, multiple DIVs may be generated in code usingConditionalStop and/or CheckHazardP instructions. The GeneratePredicatesinstruction, however, uses a single DIV to partition the vector. Thereare two methods for dealing with this situation: (1) partitioning loopscan be nested; or (2) the DIV's can be combined and used in a singlepartitioning loop. Either approach yields correct results, but theoptimal approach depends on the characteristics of the loop in question.More specifically, where multiple DIVs are expected not to havedependencies, such as when the compiler simply cannot determine aliasingon input parameters, these embodiments can combine multiple DIVs intoone, thus reducing the partitioning overhead. On the other hand, incases with an expectation of many realized memory hazards, theseembodiments can nest partitioning loops, thereby extracting the maximumparallelism possible (assuming the prospect of additional parallelismexists).

In some embodiments, DIVs can be combined using a VectorMax (A, B)instruction:

i2 = CheckHazardP(a,c,p0); // i2 = { 0 0 2 0 2 4 0 0 } i3 =CheckHazardP(b,c,p0); // i3 = { 0 0 1 3 3 0 0 0 } ix = VectorMax(i2,i3);// ix = { 0 0 2 3 3 4 0 0 }

Because the elements of a DIV should only contain numbers less than theposition of that element, which represent dependencies earlier in time,later dependencies only serve to further constrain the partitioning,which renders lower values redundant from the perspective of theGeneratePredicates instruction. Thus, taking the maximum of all DIVseffectively causes the GeneratePredicates instruction to return theintersection of the sets of elements that can safely be processed inparallel.

FIG. 8 presents a vectorized loop from program code with memory aliasingin accordance with the described embodiments. In this example noaliasing exists between C[ ] or D[ ] and A[ ], but operations on A[ ]may alias one another. If the compiler is unable to rule out aliasingwith C[ ] or D[ ], the compiler can generate additional hazard checks.Because there is no danger of aliasing in this case, the read operationson arrays C[ ] and D[ ] have been positioned outside thevector-partitioning loop, while operations on A[ ] remain within thepartitioning loop. If no aliasing actually exists within A[ ], thepartitions retain full vector size, and the partitioning loop simplyfalls through without iterating. However, for iterations where aliasingdoes occur, the partitioning loop partitions the vector to respect thedata dependencies, thereby ensuring correct operation.

In the example presented in FIG. 8, the hazard check is performed acrossthe entire vector of addresses. In the general case, however, it isoften necessary to check hazards between conditionally executed memoryoperations. The CheckHazardP instruction takes a predicate thatindicates which elements of the second memory operation are active. Ifnot all elements of the first operation are active, this CheckHazardPinstruction itself can be predicated with a zeroing predicatecorresponding to those elements of the first operand which are active.(Note that this yields correct results for cases where the first memoryoperation is predicated.)

FIG. 9 presents a vectorized loop from program code with memory aliasingin accordance with the described embodiments. As shown in FIG. 9, thetop portion is a loop with a memory hazard on array E[ ]. The codeconditionally reads and writes to unpredictable locations within thearray. The vectorized Macroscalar code for this loop is shown in thebottom portion in accordance with the described embodiments.

In the vectorized loop, p1 and p2 are predicates indicating whetherarray E[ ] is to be read or written, respectively. The CheckHazardPinstruction checks vectors of addresses (h and i) for memory hazards.The parameter p2 is passed to CheckHazardP as the predicate controllingthe second memory operation (the write). Thus, CheckHazardP identifiesthe memory hazard(s) between unconditional reads and conditional writespredicated on p2. The result of CheckHazardP is zero-predicated in p1.This places zeroes in the DIV(ix) for element positions that are not tobe read from E[ ]. Recall that a zero indicates no hazard. Thus, theresult, stored in ix, is a DIV that represents the hazards betweenconditional reads predicated on p1 and conditional writes predicated onp2. This is made possible because non-hazard conditions are representedwith a zero in the DIV.

Pseudo Loop-Carried Dependencies

The described embodiments can encounter pseudo loop-carrieddependencies. In these embodiments, not all references to valuescalculated during a previous iteration are actual loop-carrieddependencies. A common example is when a calculated value is compared toits value from the previous iteration. Such dependencies are merelyoverlapping dependencies and, hence, do not form a loop-carrieddependency chain. The following loop is used as an example:

j = 0; for (x=0; x<KSIZE; ++x) {    j = A[x] + A[x+1];    if (j != last)   {       B[x] = j;    }    last = j;    if (E[x] < RANGE)      continue;    if (C[x] < FACTOR)    {       D[x] = j;    } }

Example 4 Program Code Loop

dest=ShiftInRightP(in,vect,pred)

The ShiftInRight instruction takes the scalar value in and places in inthe leftmost position of vector vect, shifting elements of vect to theright to make room. Shifted elements are propagated across elements thathave a zero-predicate in the parameter. The shifted vector is thenstored to the destination register. For example:

Entry: in   = 9 vect  = {1 2 3 4 5 6 7 8} pred = {0 1 1 1 0 1 1 1} Exit:dest  = {9 9 2 3 4 4 6 7}

dest=Continue(pred)

The Continue instruction evaluates the predicate pred, and returns thelogical negation of each active element. In the described embodiments,this instruction can be used to handle C-language “continue” statements.In the described embodiments, the Continue instruction performs asimilar operation to the logical “not” operation. For example:

Entry: pred = {0 0 1 0 0 1 0 0} Exit: dest  = {1 1 0 1 1 0 1 1}

FIG. 10 presents a vectorized loop from program code with pseudoloop-carried dependencies in accordance with the described embodiments.As shown in FIG. 10, the vector last represents a vector of last valuesof j. It is calculated by shifting the vector j to the right oneposition. The last value of j from the previous pass is kept in thescalar q and is shifted into the earliest position of last. Likewise,the ultimate value of j from the current pass is copied into the scalarq for the next pass by the “q=j” operation.

To process the “continue” statement in this loop, the predicate p1 iscalculated to constrain the elements for the remainder of the loop. Thisis predicated on p0 so that elements previously inactive remaininactive.

Conditional Updates

The described embodiments can encounter loop-carried dependencies thatoccur as the result of conditionally updating variables in loops. Forexample, incrementing a variable in every iteration is not considered aloop-carried dependency because the variable is a priori computable by acompiler. However, if the increment occurs conditionally, then aloop-carried dependency can be created.

y = 0; for (x=0; x<KSIZE; ++x) {    B[x] = A[y];    if (A[x] < FACTOR1)      ++y;    if (y >= FACTOR2)       y = 0;    C[x] = A[y]; }

Example 5 Program Code Loop

Note that in Example 5, the variable y is used before being updated.This is a common occurrence, but this case presents another obstacle tovectorization: a vector y must be calculated to determine the values ofy before y was calculated. This loop also uses y after y is updated.

This example conditionally updates the variable y either by incrementingy or resetting y to zero. Although y is a loop-carried dependencybecause y is conditionally updated, it is important to note that thereare two situations where its value is predictable, thereby allowingparallelism: iterations where y is incremented but not reset, anditerations where y remains unchanged.

dest=IncrPropagate1(value,pred)

This instruction returns a vector corresponding to the values of avariable before the variable is incremented. Only the first activeelement of value is referenced in this calculation. The parameter preddesignates the active elements. The post-increment value is propagatedacross inactive elements. For example:

Entry: value = {2 4 2 4 2 4 2 4} pred  = {0 1 1 1 0 0 1 1} Exit: dest  ={2 4 5 6 7 7 7 8}

As shown, the first element of the vector remains unchanged because thefirst value is inactive. The second element remains unchanged becausethe second element's value before the second element is incremented isunchanged. The first active element in value (the second element in thisexample) serves as the basis for calculating the remaining activeelements.

dest=IncrPropagate2(value,pred)

This instruction returns a vector corresponding to the value of avariable after the variable is incremented. Only the first activeelement of value is referenced in this calculation. The parameter preddesignates which elements are active. The post-increment value ispropagated across inactive elements. For example:

    Entry: value = {2 4 2 4 2 4 2 4} pred  = {0 1 1 1 0 0 1 1}     Exit:dest  = {2 5 6 7 7 7 8 9} dest = DecrPropagate1(value, pred) dest =DecrPropagate2(value, pred)

These instructions are the decrementing variants of IncrPropagate1 andIncrPropagate2, respectively. As described above, these instructionsreturn a vector corresponding to the value of a variable before andafter the variable is decremented, respectively. Only the first activeelement of value is referenced in this calculation. The parameter preddesignates which elements are active. The post-decrement value ispropagated across inactive elements.

FIG. 11 presents a vectorized loop from program code with conditionalupdates in accordance with the described embodiments. As shown in FIG.11, the comparison with FACTOR1 determines whether the variable y isincremented, and the comparison with FACTOR2 determines if y is to bereset. In the vectorized code, speculative partitioning is used,speculating that variable y is not to be reset. In some embodiments,speculative partitioning always speculates in the direction that allowsthe most parallelism.

In the outer loop, Loop1, vector p2 is initially cleared in preparationfor the GeneratePredicates instruction in Loop2. Because speculativepartitioning is being used, however, any remaining elements should bedetermined before GeneratePredicates executes. The Remaining instructiondetermines the number of elements remaining to be processed.GeneratePredicates may reduce this further based on the determination ofdependencies that were not available earlier.

Consolidation

It is common for variables calculated within loops to be used after theloop terminates. In the described embodiments, because the loops beingvectorized are initially scalar, some vectors may need to be re-cast asscalars for subsequent use. We call this process “consolidation.” Theseembodiments account for the case where a loop terminates prematurely andthe most recent value written to a register may not be its final value.In this case, the final scalar value of a variable may be from anearlier write that alters an element corresponding to a later iteration.For example, in Example 6 below the final scalar value for the variabler is 15. This is easily determinable because the calculations areunconditional.

for (x=0; x<10; ++x) {   r = x * 3;   if (x == 5)     break;   r = x *2; }

Example 6 Program Code Loop

In the following loop, calculations of r are conditional, as are theconditions that can cause premature loop termination:

x = 0; for (; x<KSIZE; ++x) {   r = 23;   for (; x<KSIZE; ++x)   {    if (A[x] < FACTOR1)       r = A[x];     if (B[x] < FACTOR2)      break;     if (C[x] < FACTOR1)       r = C[x];     if (D[x] <FACTOR2)       break;     if (E[x] < FACTOR1)       r = E[x];     if(F[x] < FACTOR2)       break;   }   if (x < KSIZE)   {     G[x] = r;   }}

Example 7 Program Code Loop

dest=PreBreak(pred)

The PreBreak instruction evaluates the predicate pred, which indicateswhich iteration (if any) prematurely exits the loop via a C-style“break” instruction. The instruction returns a predicate indicatingwhich loop iterations should be active before the break occurs. Forexample:

Entry: pred = { 0 0 0 1 0 1 0 0 } Exit: dest = { 1 1 1 1 0 0 0 0 }

dest=Break(pred)

The Break instruction evaluates the predicate pred, which indicateswhich iteration (if any) prematurely exits the loop via a C-style“break” instruction. The instruction returns a predicate indicatingwhich loop iterations should be active after the break occurs. Forexample:

Entry: pred = { 0 0 0 1 0 1 0 0 } Exit: dest = { 1 1 1 0 0 0 0 0 }

dest=CopyPropagate(dest,src,pred)

The CopyPropagate instruction copies active elements of src, asdetermined by pred, into the destination dest. The copy operationeffectively progresses left to right. Any inactive elements prior to thefirst active element remain unchanged in dest. From this point forward,active elements are copied from src to dest. For inactive elements, thelast active element of src is propagated into dest. For example:

Entry: dest = { 1 2 3 4 5 6 7 8 } src = { 9 A B C D E F 0 } pred = { 0 11 1 0 0 1 1 } Exit: dest = { 1 A B C C C F 0 }

FIGS. 12 and 13 present a vectorized loop from program code withconsolidation in accordance with the described embodiments. In thesefigures, FIG. 12 includes the original source code, while FIGS. 13A-13Binclude the vectorized code.

FIGS. 12 and 13A-13B illustrate a general case of consolidatingvariables in the presence of premature loop termination. For eachpossible early termination, both the Break and PreBreak results arerecorded, predicated on any prior termination condition. Before thearray G[ ] can be written, the correct value of r should be consolidatedfrom the various conditional reads that may have occurred. To accomplishthis, the shortest extent of the PreBreak conditions is used to mask theconditional read predicates, which are OR-ed together. This indicateswhich element of r is propagated. The CopyPropagate operation thenpropagates this value to the end of the vector. The scalar value of r isthen extracted using the PreBreak extent that was calculated earlier.

Interdependent Read-After-Write

Data dependencies in scalar code generally occur in a simple “to-from”form. When such code is vectorized, dependencies between vector elements(corresponding to instances of scalar variables in time) remain in theto-from form. However, because executing the vector instructions cancoalesce operations on variables that occur at different times in thescalar version of the code; paradoxical vector interdependencies can becreated. For example, consider the example loop and the dependency chartshown in FIG. 14. In the example loop, the scalar dependencies arestraightforward. The vectorization of this simple source code iscomplicated because in order to perform the write operation, the systemneeds a vector of t indices, but using the read operation to obtain avector of t indices potentially depends on the write occurring first.

This situation is unique to vectorized code. Scalar code does not sufferthis phenomenon because dependencies occur in different iterations atdifferent times. Another situation that can cause a mutual dependency iswhen a write occurs before a conditional break from a loop. If thetermination test depends on the write, a mutual dependency is createdbecause the existence of the write depends on the loop not terminatingprematurely.

Consider the two loops shown in Example 8A and 8B, below. Example 8Acontains a write to A[ ] between two reads of A[ ]. Because the writedepends on the variable k, and k is potentially dependent upon thewrite, a mutual dependence is created. Example 8B also has a mutualdependence, except that half of the mutual dependence is a control-flowdependency, while the other half is a data dependency. The write to B[ ]can only be allowed in iterations that should actually execute, but thebreak depends on the write to B[ ].

k = 99; for (x=0; x<KSIZE; ++x) {   j = A[D[x]];   A[C[x]] = j + k;   k= A[B[x]];   E[x] = k; }

Example 8A Program Code Loop

for (x=0; x<KSIZE; ++x) {   t = A[x];   B[x] = t;   if (B[t] < FACTOR)    break;   C[x] = B[t]; }

Example 8B Program Code Loop

dest=CheckHazardPx(first,second,pred)

CheckHazardPx evaluates two vectors of addresses/indices for potentialmemory hazards, where the operations may be interdependent. Thisinstruction is similar to the CheckHazardP instruction. UnlikeCheckHazardP, where each element of first is checked only againstlesser-ordered elements in second, CheckHazardPx also checksequal-numbered element positions in second. If these overlap then theelement position with the hazard is recorded in the next highest elementposition. For example:

Entry: first = { 1 2 3 4 5 6 7 8 } second = { 3 1 2 3 5 4 5 6 } pred = {1 1 1 1 1 1 1 1 } Exit: dest = { 0 0 1 0 0 5 0 0 }

FIGS. 15-16 present two loops from program code with mutual dependencesalong with vectorized versions of these loops in accordance with thedescribed embodiments. In the example shown in FIG. 15, before writingto A[C[x]] it is necessary to perform the potentially dependent read ofA[B[x]] so that a vector of k can be calculated. This is accomplishedwith a vector-partitioning loop based on CheckHazardPx to break thevector where k actually depends on the write to A[ ]. To functioncorrectly where a single memory location is written and then read in thesame iteration, the original read is also required after the write. Inpractice this does not substantially affect performance because allvalues have been recently accessed and are in the cache. The dependencebetween A[D[x]] and A[B[x]] is handled with a CheckHazardP instruction.Because k is calculated in the iteration before k is used, theShiftInRight instruction is used to move the data into the correctelement position.

In the example shown in FIG. 16, the loop may prematurely exit based ondata read from B[t], which occurs after the write to B[x]. The sametechnique is applied as in FIG. 15, but the data read from B[t] is usedto calculate whether a premature exit occurs and limit the writeaccordingly, thus ensuring that no writes occur for loop iterations thatwould not have executed in a sequential machine. Due to the possibilityof premature exit, when B[t] is re-read the terminating condition shouldbe re-calculated to ensure all loop exits are properly recognized.

Summation

A common operation is calculating the sum of a series of items. If therunning sum result is referenced within the loop, the result should becalculated each step of the way. Consider the following loop where aconditional running sum is stored to array B[ ].

v = v2 = 0; for (x=0; x<KSIZE; ++x) {   if (A[x] < FACTOR1)     v =A[x];   B[v] = v2;   if (A[x] > FACTOR2 && A[x] < FACTOR3)     continue;  v2 += v; }

Example 9 Program Code Loop

dest=RunningSum1P(base,addend,pred)

This instruction returns a vector corresponding to the value of avariable before a recursive add is performed on the variable. Only thefirst active element of vector base is used in this calculation. Thevector parameter addend holds the values that are added to base. Thevector parameter pred designates which elements are active. The post-addvalue is propagated across inactive elements. For example:

Entry: value = { 3 4 3 4  3  4  3  4 } addend = { 2 3 2 3  2  3  2  3 }pred = { 0 1 1 1  0  0  1  1 } Exit: dest = { 3 4 7 9 12 12 12 14 }

As shown above, the first element in the vector remains unchangedbecause it is inactive. The second element in the vector remainsunchanged because the element retains its value before the addend isadded to the element. The first active element in base (the secondelement in base) is the basis for the remaining calculations.

dest=RunningSum2P(base,addend,pred)

This instruction returns a vector corresponding to the value of avariable after an addend gets recursively added to it. Only the firstactive element of base is used in this calculation. The parameter addendholds the values that are added to base. The parameter pred designateswhich elements are active. The post-add value is propagated acrossinactive elements. For example:

Entry: value = { 3 4 3  4  3  4  3  4 } addend = { 2 3 2  3  2  3  2  3} pred = { 0 1 1  1  0  0  1  1 } Exit: dest = { 3 7 9 12 12 12 14 17 }

Along with the RunningSum operations, the described embodiments cansupport other operations that “run” across the vector. In theseembodiments, there can be a number of instructions that encapsulatecommonly used operations across the vector in a vector-length agnosticmanner, thereby allowing the amount of parallelism to vary due toruntime dependencies. For example, some embodiments include a runningbitwise shift instruction for those loops that perform successive shiftsto process bits of a word. In these embodiments, the running-shiftinstruction takes the number of positions to shift as a vectorparameter, as well as a predicate to indicate when shifts occur and whenthey do not. In addition, some embodiments include running bitwise andlogical operations, such as AND/OR/XOR. Moreover, some embodimentsinclude a running-multiply, which addresses common functions such asexponentiation, factorial, and Taylor-series expansion.

FIG. 17 presents a vectorized loop from program code with summation inaccordance with the described embodiments. As shown in FIG. 17, whenvectorizing the source code, the write of B[ ] is relocated to thebottom of the loop body, which allows a vector of v2 sums to becalculated before they are written. In the original loop, the value ofv2 is used before the addition occurs, which first requires aRunningSum1P instruction to calculate a vector of v2 sums correspondingto the scalar values of v2 before the add occurs. After v2 is written,RunningSum2P is used to calculate a vector of v2 sums corresponding tothe scalar values of v2 after the addition is performed. This secondstep is necessary so the next pass can use the final value, although itis often the case that it is also needed later in the same pass.

A Complex Example

Example 10, below, presents a loop in program code that includes manyobstacles to conventional vectorization: pointer chasing, addressaliasing, irregular memory addressing, a data-serial function,unpredictable loop exit conditions, and loop-carried dependencies.Previous examples have illustrated how the described embodiments (i.e.,the Macroscalar architecture) address loop-carried dependencies,unpredictable loop termination, and irregular memory addressing. Example10 introduces two varieties of data-serial dependency chains. The firstis pointer-chasing, contained in the z=A[z] statement in the sourcecode. While pointer-chasing itself cannot be vectorized, many loopscontaining pointer chasing can, as is shown in this example. The seconddata-serial chain is an inner loop which performs an integer square-rootcalculation. In this inner loop, every iteration depends on the resultsof the prior iteration, and the loop exit depends on the final iterationand, therefore, cannot be predicted.

y = z = 0; for (x=0; x<LIMIT; ++x) {   z = A[z];   t = B[z];   tx = t;  ty = t / tx;   while (ty < tx)   {     tx = (tx + ty) / 2;     ty = t/ tx;   }   if (tx < FACTOR)   {     A[y++] = tx;   } }

Example 10 Program Code Loop

FIGS. 18A-18B present a vectorized loop from program code with summationin accordance with the described embodiments. As shown in FIG. 18B, theinstruction VectorIndex (0, 1) is used to create a DIV that partitionsthe vector into single-element sub-vectors, serializing the pointerchase.

In all previous examples, vectorization was performed “horizontally,”that is, each element of the Macroscalar vectors represents acorresponding iteration of the loop. In this example there are twoloops: The outer “for” loop, and the inner “while” loop. The enclosing“for” loop is, as before, horizontally vectorized, but the inner “while”loop is vertically vectorized. In a vertically vectorized loop, thedescribed embodiments process the iterations of the loop sequentially intime, just like a scalar loop, but the data is a vector rather than ascalar loop. In vertically vectorized loops, the vector of data beingprocessed corresponds to multiple iterations of the enclosing loop. Inother words, a horizontal vector built in the enclosing “for” loop isiterated sequentially until the termination conditions of the “while”loop are met for all elements of the vector.

Horizontal and Vertical Vectorization

In the preceding examples of the described embodiments, verticalvectorization was applied because horizontal vectorization was notpossible due to serial dependence in the inner loop. However, verticalvectorization is an efficient vectorization technique in its own right,as demonstrated below.

for (x=0; x<LIMIT; ++x) {   t = A[x];   s = 0;   for (y=0; y<t; ++y)   {    s += A[x+y];   }   B[x] = s; }

Example 11 Program Code Loop

The loop in Example 11 encloses a simple summation loop, which is bothhorizontally and vertically vectorized to illustrate the operationsperformed by the described embodiments. When the inner loop ishorizontally vectorized, a scalar region is crafted around the innerloop that removes it from the surrounding vector context.

FIGS. 19A-19B present a vectorized loop from program code that has beenvectorized both horizontally and vertically in accordance with thedescribed embodiments. Note that the code from Example 11 above is thecode that has been vectorized in the examples in FIGS. 19A-19B.

Vectorizing Functions and Function Calls

In some embodiments, the compiler can replicate a function with aconventional scalar interface (i.e., a version of the function withscalar inputs and outputs) and create a secondary version with a vectorinterface (or can create a version with the vector interface alone).Such functions are typically vertically vectorized, for efficiency andsimplicity, although horizontal vectorization may also be applied. Inthese embodiments, function vectorization is most easily achieved incases where the function has no side effects on global, file-scopestatic, or function-local static storage. Functions using straight-linecode (without loops) can also be vertically vectorized, operating oneither full or partial vectors under the control of a predicate passedto the function in the compiler-generated vector interface.

If only “safe” functions are vectorized, the existence of the secondaryvariant guarantees that the function can safely and effectively becalled from within a vectorized loop. Thus, in the describedembodiments, Macroscalar vectorization techniques can be applied tovectorize commonly used functions such as sin( ), cos( ), tan( ), atan(), sqrt( ), etc. Doing so enables loops using these functions to callvector variants with vector interfaces, rather than incurring thebottleneck of a scalar interface.

While the scheme above works for file-local functions, library functionslike sin( ) have interfaces established through header files. Becausecompilers in the described embodiments generally do not make autonomousmodification of header files, in some embodiments, an annotationmechanism, such as compiler-generated XML files stored in standardizedlocations, provides additional compiler-generated source-level inputdescribing the secondary compiler-generated vector function interfaces.In these embodiments, the compiler attempts to open these annotationfiles implicitly upon inclusion of the appropriate header file. Forexample, upon inclusion of the header file <stdlib.h>, the compiler alsoattempts to load <stdlib.xml>. If the file did not exist, then thecompiler presumes that no vector interfaces existed.

In the described embodiments, the annotation mechanism also has thepotential to enable vectorization in other ways. For example, assume aloop calling two functions, one at the top of the loop body, foo( ), andthe other near the bottom, bar( ). In the absence of any deepinformation about these functions beyond their interfaces, the compileris forced to generate program code wherein the functions and the entirebody of code between them execute serially, one element at a time. Forexample, foo( ) might call srand( ), while bar( ) might call rand( ).Calling srand( ) multiple times before calling rand( ) is incorrect. If,on the other hand, the functions modify no non-local state, the compilervectorizes the code between the two function calls. The annotationscheme mentioned above can be extended to provide information about thefunctions and their descendants that enable the compiler to vectorizesuch code.

In the extended annotation scheme in the described embodiments,attributes of functions are propagated up through their callers tocommunicate key information to the compiler about all the functionscalled from a loop. Information as to whether the functions modifyfunction-scope static variables, pointed-to function parameters,file-scope static variables, or global variables is enormously valuable.For file-scope static variables, for example, the type of reference(read or write), the name of the variable, and the file where they arelocated might be communicated. For global variables, only the name andtype of reference is necessary. Knowledge that a function modifies noparameters or static state enables the compiler in the describedembodiments to forgo enforcement of atomicity on that function, therebyremoving that obstacle to vectorization.

The process in these embodiments is caller-guaranteed-correctness,assisted by trustable hints about the functions being called. Theabsence of a hint may cause the preclusion of an optimization, but isalways safe. In these embodiments, annotations begin at the bottom levelof the library, because non-annotated functions propagate uncertaintyupward, disabling many opportunities for vectorization.

Instruction Definitions

The following section contains exemplary instructions used in thedescribed embodiments (i.e., that are included in Macroscalararchitecture). The described instructions demonstrate the concepts usedin implementing the Macroscalar architecture and therefore do notcomprise a complete list of the possible instructions. A person of skillin the art will recognize that these concepts may be implemented usingdifferent arrangements or types of instructions without departing fromthe spirit of the described embodiments.

Unlike conventional single-instruction-multiple-data (SIMD) coding, insome embodiments, Macroscalar code can combine vector variables withscalar registers or immediate values. Thus, in these embodiments,Macroscalar instructions can directly reference scalar registers andimmediate values without making unnecessary vector copies of them. Notethat this can help avoid unnecessary vector-register pressure within aloop because more vector registers can be available instead of beingrequired for making vector copies of scalars or immediate values.

We describe these instructions using a signed-integer data type.However, in alternative embodiments, other data types or formats areused. Moreover, although Macroscalar instructions may take vector,scalar, or immediate arguments in practice, only vector arguments areshown here to avoid redundancy.

The descriptions of the instructions reference vector elements with azero-based numbering system (i.e., element “0” is the first element).However, certain instructions, such as those involved in the processingof DIVs, express dependencies using 1-based element numbering, eventhough they are actually implemented using 0-based element numbering.Care should be taken to avoid confusing the language the results areexpressed in from the language used to implement the instructions.

For the purposes of explanation, the vector data type is defined as aC++ class containing an array v[ ] of elements that comprise the vector.Within these descriptions, the variable VECLEN indicates the size of thevector. In some embodiments, VECLEN is constant.

In the following examples, predication is communicated to theinstructions via two variables. The vector gPred is the predicate vectorthat affects the instruction and/or the assignment of the result vector.A scalar variable, gPredFlag, indicates whether gPred functions in apredication or zeroing capacity. This variable is set to 0 when azeroing predicate is being applied, or is set to 1 otherwise.Additionally, some instructions may reference gPred to affect theoperation of the instruction apart from the final assignment. If aninstruction is not predicated, then all elements are considered active,and the vector gPred contains all true indicators.

Note that the format of the following instruction definitions is astatement of the instruction type followed by a description of theinstruction that can include example code as well as one or more usageexamples.

Vector Assignment

During vector assignment, active vector elements, as determined byoptional predication, are copied into a destination vector. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication.

Predication and zeroing are applied at the assignment of the resultvector. Final assignment of the result vector is an implicit or explicitpart of every instruction that produces a result. Note that theassignment operator performs this function where predication or zeroingis not performed explicitly by the instruction.

const Vector Vector::operator = (const Vector &val) {   for (int x=0;x<VECLEN; ++x)     if (gPred.v[x])       v[x] = val.v[x];     else      v[x] &= −gPredFlag;  // Not changed       if predicated, 0 ifZeroed   return(*this); }

Examples

~p0; a = b; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 9 9 } b= { 1 2 3 4 5 6 7 8 } On Exit: a = { 9 9 3 4 5 6 9 9 } !p0; a = b; OnEntry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 9 9 } b = { 1 2 3 4 56 7 8 } On Exit: a = { 0 0 3 4 5 6 0 0 }

Scalar Assignment

A scalar register or immediate value is copied into active elements ofthe destination vector, as determined by optional predication. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication.

const Vector & Vector::operator = (const _nt val) {   int x;   for (x=0;x<VECLEN; ++x)     if (gPred.v[x])       v[x] = val;     else       v[x]&= −gPredFlag;  // Not changed       if predicated, 0 if Zeroed  return(*this); }

Examples

~p0; a = b; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 9 9 } b= 5 On Exit: a = { 9 9 5 5 5 5 9 9 } !p0; a = b; On Entry: p0 = { 0 0 11 1 1 0 0 } a = { 9 9 9 9 9 9 9 9 } b = 5 On Exit: a = { 0 0 5 5 5 5 0 0}

Scalar Cast

This instruction casts a vector of values into a scalar register. Onlythe last active element, as determined by optional predication, iscopied to the scalar destination. If no elements are active, thisinstruction returns the first element of the vector.

Vector::operator int (void) const {   int x,rc;    for (x=VECLEN−1;x>=0; −−x) // Locate last    active element       if (gPred.v[x])        break;       rc = v[0]; // Use first element if there       areno ones       if (x >= 0)         rc = v[x];   return(rc); }

Examples

~p0; a = int(b); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = 2 b = { 1 2 3 45 6 7 8 } On Exit: a = 6 !p0; a = int(b); On Entry: p0 = { 0 0 1 1 1 1 00 } a = 2 b = { 1 2 3 4 5 6 7 8 } On Exit: a = 6

VectorNeg

This instruction mathematically negates active vector elements. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction.

Vector VectorNeg(const Vector &ob) {   Vector result;   for (int x=0;x<VECLEN; ++x)     result.v[x] = −ob.v[x];   return(result); }

Examples

~p0; a = −b; On Entry: p0 = {  0  0  1 1  1  1 0 0 } a = {  9  9  9 9  9 9 9 9 } b = {−3 −2 −1 0  1  2 3 4 } On Exit: a = {  9  9  1 0 −1 −2 9 9} !p0; a = −b; On Entry: p0 = {  0  0  1 1  1  1 0 0 } a = {  9  9  9 9 9  9 9 9 } b = {−3 −2 −1 0  1  2 3 4 } On Exit: a = {  0  0  1 0 −1 −20 0 }

VectorNot

This instruction logically negates active vector elements. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction.

Vector VectorNot(const Vector &ob) {   Vector result;   for (int x=0;x<VECLEN; ++x)     result.v[x] = !ob.v[x];   return(result); }

Examples

~p0; a = VectorNot(b); On Entry: p0 = { 0  0  1 1 1 1 0 0 } a = { 9  9 9 9 9 9 9 9 } b = { 0 −2 −1 0 1 2 3 0 } On Exit: a = { 9  9  0 1 0 0 99 } !p0; a = VectorNot(b); On Entry: p0 = { 0  0  1 1 1 1 0 0 } a = { 9 9  9 9 9 9 9 9 } b = { 0 −2 −1 0 1 2 3 0 } On Exit: a = { 0  0  0 1 0 00 0 }

VectorInv

This instruction performs bitwise inversion active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. As shown below, in someembodiments, the instruction processes all elements equivalently;however, predication is performed by the assignment of the result, andshould be considered an integral part of this instruction.

Vector VectorInv(const Vector &ob) {   Vector result;   for (int x=0;x<VECLEN; ++x)     result.v[x] = ~ob.v[x];   return(result); }

Examples

~p0; a = ~b; On Entry: p0 = {  0  0  1  1  1  1 0 0 } a = {  9  9  9  9 9  9 9 9 } b = {−3 −2 −1  0  1  2 3 4 } On Exit: a = {  9  9  0 −1 −2−3 9 9 } !p0; a = ~b; On Entry: p0 = {  0  0  1  1  1  1 0 0 } a = {  9 9  9  9  9  9 9 9 } b = {−3 −2 −1  0  1  2 3 4 } On Exit: a = {  0  0 1  0 −1 −2 0 0 }

VectorAdd

This instruction performs addition on active vector elements. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication. In this implementation, the instructiontakes the result vector as an input and performs predication explicitly.

Vector VectorAdd (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] + val.v[x]);     else       result->v[x] &= −gPredFlag;   //Not       changed if predicated, 0 if Zeroed   return(*result); }

Examples

~p0; a = b + c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 9 9} b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit: a = { 9 9 8 88 8 9 9 } !p0; a = b + c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 99 9 9 9 9 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit: a ={ 0 0 8 8 8 8 0 0 }

VectorSub

This instruction performs subtraction on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. This implementation of theinstruction takes the result vector as an input and performs predicationexplicitly.

Vector VectorSub (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] − val.v[x]);     else       result->v[x] &= −gPredFlag;  // Not      changed if predicated, 0 if Zeroed   return(*result); }

Examples

~p0; a = b − c; On Entry: p0 = { 0 0 1 1 1  1 0 0 } a = { 9 9 9 9 9  9 99 } b = { 8 7 6 5 4  3 2 1 } c = { 0 1 2 3 4  5 6 7 } On Exit: a = { 9 94 2 0 −2 9 9 } !p0; a = b − c; On Entry: p0 = { 0 0 1 1 1  1 0 0 } a = {9 9 9 9 9  9 9 9 } b = { 8 7 6 5 4  3 2 1 } c = { 0 1 2 3 4  5 6 7 } OnExit: a = { 0 0 4 2 0 −2 0 0 }

VectorMult

This instruction performs multiplication on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. This implementation of theinstruction takes the result vector as an input and performs predicationexplicitly.

Vector VectorMult (const Vector &ob, const Vector &val, Vector *result){   for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x]= (ob.v[x] * val.v[x]);     else       result->v[x] &= −gPredFlag;  //Not       changed if predicated, 0 if Zeroed   return(*result); }

Examples

~p0; a = b * c; On Entry: p0 = { 0 0  1  1  1  1 0 0 } a = { 9 9  9  9 9  9 9 9 } b = { 8 7  6  5  4  3 2 1 } c = { 0 1  2  3  4  5 6 7 } OnExit: a = { 9 9 12 15 16 15 9 9 } !p0; a = b * c; On Entry: p0 = { 0 0 1  1  1  1 0 0 } a = { 9 9  9  9  9  9 9 9 } b = { 8 7  6  5  4  3 2 1 }c = { 0 1  2  3  4  5 6 7 } On Exit: a = { 0 0 12 15 16 15 0 0 }

VectorDiv

This instruction performs division on active vector elements. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication. This implementation of the instructiontakes the result vector as an input and performs predication explicitly.

Vector VectorDiv (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] / val.v[x]);     else       result->v[x] &= −gPredFlag;  // Not      changed if predicated, 0 if zeroed   return(*result); }

Examples

~p0; a = b / c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  = { 9 9 9 9 9 9 99 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } On Exit: a  = { 99 3 1 1 0 9 9 } !p0; a = b / c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  ={ 9 9 9 9 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } OnExit: a  = { 0 0 3 1 1 0 0 0 }

VectorAnd

This instruction performs a bitwise “and” on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. This implementation of theinstruction takes the result vector as an input and performs predicationexplicitly.

Vector VectorAnd (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] & val.v[x]);     else       result->v[x] &= −gPredFlag; // Not      changed if predicated, 0 if zeroed   return(*result); }

Examples

~p0; a = b & c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  = { 9 9 9 9 9 9 99 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } On Exit: a  = { 99 2 1 4 1 9 9 } !p0; a = b & c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  ={ 9 9 9 9 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } OnExit: a  = { 0 0 2 1 4 1 0 0 }

VectorOr

This instruction performs a bitwise “or” on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. This implementation of theinstruction takes the result vector as an input and performs predicationexplicitly.

Vector VectorOr (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] | val.v[x]);     else       result->v[x] &= −gPredFlag; // Not      changed if predicated, 0 if zeroed   return(*result); }

Examples

~p0; a = b | c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  = { 9 9 9 9 9 9 99 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } On Exit: a  = { 99 6 7 4 7 9 9 } !p0; a = b | c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  ={ 9 9 9 9 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } OnExit: a  = { 0 0 6 7 4 7 0 0 }

VectorXor

This instruction performs a bitwise “xor” on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. This implementation of theinstruction takes the result vector as an input and performs predicationexplicitly.

Vector VectorXor (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] {circumflex over ( )} val.v[x]);     else       result->v[x] &=−gPredFlag; // Not       changed if predicated, 0 if zeroed  return(*result); }

Examples

~p0; a = b {circumflex over ( )} c; On Entry: p0 = { 0 0 1 1 1 1 0 0 }a  = { 9 9 9 9 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7} On Exit: a  = { 9 9 4 6 0 6 9 9 } !p0; a = b {circumflex over ( )} c;On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  = { 9 9 9 9 9 9 9 9 } b  = { 8 7 65 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } On Exit: a  = { 0 0 4 6 0 6 0 0 }

VectorShL

This instruction performs a bitwise left-shift on active vectorelements. Inactive elements either remain unmodified, or are forced tozero, depending on the nature of the predication. This implementation ofthe instruction takes the result vector as an input and performspredication explicitly.

Vector VectorShL (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] << val.v[x]);     else       result->v[x] &= −gPredFlag; // Not      changed if predicated, 0 if zeroed   return(*result); }

Examples

~p0; a = b << c; On Entry: p0 = { 0 0  1  1  1  1 0 0 } a  = { 9 9  9  9 9  9 9 9 } b  = { 8 7  6  5  4  3 2 1 } c  = { 0 1  2  3  4  5 6 7 } OnExit: a  = { 9 9 24 40 64 96 9 9 } !p0; a = b << c; On Entry: p0 = { 0 0 1  1  1  1 0 0 } a  = { 9 9  9  9  9  9 9 9 } b  = { 8 7  6  5  4  3 21 } c  = { 0 1  2  3  4  5 6 7 } On Exit: a  = { 0 0 24 40 64 96 0 0 }

VectorShR

This instruction performs a bitwise right-shift on active vectorelements. Inactive elements either remain unmodified, or are forced tozero, depending on the nature of the predication. This implementation ofthe instruction takes the result vector as an input and performspredication explicitly.

Vector VectorShR (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] >> val.v[x]);     else       result->v[x] &= −gPredFlag; // Not      changed if predicated, 0 if zeroed   return(*result); }

Examples

~p0; a = b {circumflex over ( )} c; On Entry: p0 = { 0 0 1 1 1 1 0 0 }a  = { 9 9 9 9 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7} On Exit: a  = { 9 9 1 0 0 0 9 9 } !p0; a = b {circumflex over ( )} c;On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  = { 9 9 9 9 9 9 9 9 } b  = { 8 7 65 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } On Exit: a  = { 0 0 1 0 0 0 0 0 }

VectorMin

This instruction performs a “min” operation on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. As shown below, in someembodiments, the instruction processes all elements equivalently;however, predication is performed by the assignment of the result, andshould be considered an integral part of this instruction.

Vector VectorMin(Vector &a, Vector &b) {   Vector r;   for (int x=0;x<VECLEN; ++x)     if (a.v[x] < b.v[x])       r.v[x] = a.v[x];     else      r.v[x] = b.v[x];   return(r); }

Examples

~p0; a = VectorMin(b,c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  = { 9 9 99 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } On Exit:a  = { 9 9 2 3 4 3 9 9 } !p0; a = VectorMin(b,c); On Entry: p0 = { 0 0 11 1 1 0 0 } a  = { 9 9 9 9 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 12 3 4 5 6 7 } On Exit: a  = { 0 0 2 3 4 3 0 0 }

VectorMax

This instruction performs a “max” operation on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. As shown below, in someembodiments, the instruction processes all elements equivalently;however, predication is performed by the assignment of the result, andshould be considered an integral part of this instruction.

Vector VectorMax(Vector &a, Vector &b) {   Vector r;   for (int x=0;x<VECLEN; ++x)     if (a.v[x] > b.v[x])       r.v[x] = a.v[x];     else      r.v[x] = b.v[x];   return(r); }

Examples

~p0; a = VectorMax(b,c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a  = { 9 9 99 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 1 2 3 4 5 6 7 } On Exit:a  = { 9 9 6 5 4 5 9 9 } !p0; a = VectorMax(b,c); On Entry: p0 = { 0 0 11 1 1 0 0 } a  = { 9 9 9 9 9 9 9 9 } b  = { 8 7 6 5 4 3 2 1 } c  = { 0 12 3 4 5 6 7 } On Exit: a  = { 0 0 6 5 4 5 0 0 }

VectorMod

This instruction performs a modulus operation on active vector elements.Inactive elements either remain unmodified, or are forced to zero,depending on the nature of the predication. This implementation of theinstruction takes the result vector as an input and performs predicationexplicitly.

Vector VectorMod (const Vector &ob, const Vector &val, Vector *result) {  for (int x=0; x<VECLEN; ++x)     if (gPred.v[x])       result->v[x] =(ob.v[x] % val.v[x]);     else       result->v[x] &= −gPredFlag; // Not      changed if predicated, 0 if zeroed   return(*result); }

Examples

~p0; a = b % c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 9 9} b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit: a = { 9 9 0 20 3 9 9 } !p0; a = b % c; On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 99 9 9 9 9 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit: a ={ 0 0 0 2 0 3 0 0 }

VectorEQ

This instruction compares active vector elements and returns a resultvector indicating whether the elements of the first parameter are equalto elements of the second parameter. Inactive elements either remainunmodified, or are forced to zero, depending on the nature of thepredication. This implementation of the instruction takes the resultvector as an input and performs predication explicitly.

Vector VectorEQ (const Vector &ob, const Vector &val) {   Vector result;  for (int x=0; x<VECLEN; ++x)     result.v[x] = (ob.v[x] == val.v[x]);  return(result); }

Examples

~p0; a = (b == c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 99 9 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit: a = { 9 90 0 1 0 9 9 } !p0; a = (b == c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a ={ 9 9 9 9 9 9 9 9 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } OnExit: a = { 0 0 0 0 1 0 0 0 }

VectorGT

This instruction compares active vector elements and returns a resultvector indicating whether the elements of the first parameter aregreater-than elements of the second parameter. Inactive elements eitherremain unmodified, or are forced to zero, depending on the nature of thepredication. This implementation of the instruction takes the resultvector as an input and performs predication explicitly.

Vector VectorGT (const Vector &ob, const Vector &val) {   Vector result;  for (int x=0; x<VECLEN; ++x)     result.v[x] = (ob.v[x] > val.v[x]);  return(result); }

Examples

~p0; a = (b > c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 99 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit: a = { 9 9 11 0 0 9 9 } !p0; a = (b > c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 99 9 9 9 9 9 9 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit:a = { 0 0 1 1 0 0 0 0 }

VectorGE

This instruction compares active vector elements and returns a resultvector indicating whether the elements of the first parameter aregreater-than or equal-to elements of the second parameter. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication. This implementation of the instructiontakes the result vector as an input and performs predication explicitly.

Vector VectorGE (const Vector &ob, const Vector &val) {   Vector result;  for (int x=0; x<VECLEN; ++x)     result.v[x] = (ob.v[x] >= val.v[x]);  return(result); }

Examples

~p0; a = (b >= c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 99 9 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } On Exit: a = { 9 91 1 1 0 9 9 } !p0; a = (b >= c); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a ={ 9 9 9 9 9 9 9 9 } b = { 8 7 6 5 4 3 2 1 } c = { 0 1 2 3 4 5 6 7 } OnExit: a = { 0 0 1 1 1 0 0 0 }

Any

This instruction tests if any active element in the input is non-zero,and returns a flag indicating this.

int ANY(Vector &p) {   int s = 0;   for (int x=0; x<VECLEN; ++x)     s|= (gPred.v[x] && p.v[x]);   return(s); }

Examples

~p0; flag = ANY(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 0 0 0 1 0 00 0 } On Exit: flag = set !p0; flag = ANY(a); On Entry: p0 = { 0 0 1 1 11 0 0 } a = { 0 0 0 1 0 0 0 0 } On Exit: flag = set ~p0; flag = ANY(a);On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 1 1 0 0 0 0 1 1 } On Exit: flag= clear !p0; flag = ANY(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 1 10 0 0 0 1 1 } On Exit: flag = clear

All

This instruction tests if all active elements in the input are non-zero,and returns a flag indicating this.

int ALL(Vector &p) {   int s,t;   s = t = 0;   for (int x=0; x<VECLEN;++x)   {     t += (gPred.v[x]);     s += (gPred.v[x] && p.v[x] );   }  return(s==t); }

Examples

~p0; flag = ALL(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 0 0 1 1 1 10 0 } On Exit: flag = set !p0; flag = ALL(a); On Entry: p0 = { 0 0 1 1 11 0 0 } a = { 0 0 1 1 1 1 0 0 } On Exit: flag = set ~p0; flag = ALL(a);On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 1 1 1 0 1 1 1 1 } On Exit: flag= clear !p0; flag = ALL(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 1 11 1 0 1 1 1 } On Exit: flag = clear

None

This instruction tests if no active elements in the input are non-zero,and returns a flag indicating this.

int NONE(Vector &p) {   int s = 0;   for (int x=0; x<VECLEN; ++x)     s|= (gPred.v[x] && p.v[x]);   return(s==0); }

Examples

~p0; flag = NONE(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 1 1 0 0 00 1 1 } On Exit: flag = set !p0; flag = NONE(a); On Entry: p0 = { 0 0 11 1 1 0 0 } a = { 1 1 0 0 0 0 1 1 } On Exit: flag = set ~p0; flag =NONE(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 0 0 0 1 0 0 0 0 } OnExit: flag = clear !p0; flag = NONE(a); On Entry: p0 = { 0 0 1 1 1 1 0 0} a = { 0 0 0 1 0 0 0 0 } On Exit: flag = clear

First

This instruction tests if the first active element in the input isnon-zero, and returns a flag.

int FIRST(Vector &p) {   int s = 0;   for (int x=0; x<VECLEN; ++x)   {    s = (gPred.v[x] && p.v[x]);     if (gPred.v[x])       break;   }  return(s); }

Examples

~p0; flag = FIRST(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 0 0 1 0 01 0 0 } On Exit: flag = set !p0; flag = FIRST(a); On Entry: p0 = { 0 0 11 1 1 0 0 } a = { 0 0 1 0 0 1 0 0 } On Exit: flag = set ~p0; flag =FIRST(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 1 1 0 1 1 0 1 1 } OnExit: flag = clear !p0; flag = FIRST(a); On Entry: p0 = { 0 0 1 1 1 1 00 } a = { 1 1 0 1 1 0 1 1 } On Exit: flag = clear

Last

This instruction tests if the last active element in the input isnon-zero, and returns a flag.

int LAST(Vector &p) {   int s = 0;   for (int x=VECLEN−1; x>=0; −−x)   {    s = (gPred.v[x] && p.v[x]);     if (gPred.v[x])       break;   }  return(s); }

Examples

~p0; flag = LAST(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 0 0 1 0 01 0 0 } On Exit: flag = set !p0; flag = LAST(a); On Entry: p0 = { 0 0 11 1 1 0 0 } a = { 0 0 1 0 0 1 0 0 } On Exit: flag = set ~p0; flag =LAST(a); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 1 1 0 1 1 0 1 1 } OnExit: flag = clear !p0; flag = LAST(a); On Entry: p0 = { 0 0 1 1 1 1 0 0} a = { 1 1 0 1 1 0 1 1 } On Exit: flag = clear

Continue

This instruction logically negates active vector elements. Inactiveelements either remain unmodified, or are forced to zero, depending onthe nature of the predication. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction.

Vector Continue(Vector &p) {   Vector r;   for (int x=0; x<VECLEN; ++x)    r.v[x] = !p.v[x];   return(r); }

Examples

~p0; a = Continue(b); On Entry: p0 = { 0   0   1 1 1 1 0 0 } a = { 9   9  9 9 9 9 9 9 } b = { 0 −2 −1 0 1 2 3 0 } On Exit: a = { 9   9   0 1 0 09 9 } !p0; a = Continue(b); On Entry: p0 = { 0   0   1 1 1 1 0 0 } a = {9   9   9 9 9 9 9 9 } b = { 0 −2 −1 0 1 2 3 0 } On Exit: a = { 0   0   01 0 0 0 0 }

Break

This instruction returns a vector containing ones in all positionsbefore the first non-zero element position of its input, and zeroes allother positions. Predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector Break(Vector &p) {   Vector r = 0;   for (int x=0; x<VECLEN; ++x)  {     if (p.v[x])       break;     r.v[x] = 1;   }   return(r); }

Examples

~p0; a = Break(b); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 99 9 } b = { 0 1 0 0 1 0 0 0 } On Exit: a = { 9 9 1 1 0 0 9 9 } !p0; a =Break(b); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 9 9 } b ={ 0 1 0 0 1 0 0 0 } On Exit: a = { 0 0 1 1 0 0 0 0 }

PreBreak

This instruction returns a vector containing zeroes in all positionsafter the first non-zero element position of its input, and ones in allother positions. Predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector PreBreak(Vector &p) {   Vector r = 0;   for (int x=0; x<VECLEN;++x)   {     r.v[x] = 1;     if (p.v[x])       break;   }   return(r); }

Examples

~p0; a = PreBreak(b); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 99 9 9 } b = { 0 1 0 0 1 0 0 0 } On Exit: a = { 9 9 1 1 1 0 9 9 } !p0; a= PreBreak(b); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 9 9} b = { 0 1 0 0 1 0 0 0 } On Exit: a = { 0 0 1 1 1 0 0 0 }

ConditionalStop

This instruction takes the scalar parameter mode, which indicates anynumber of four possible transitions between true and false values ofadjacent elements in predicate p that imply data dependencies. Theparameter mode is a 4-bit field, the bits of which are defined asfollows:

-   -   kTF—Implies a loop-carried dependency from an iteration for        which the predicate is true, to the subsequent iteration for        which the value of the predicate is false.    -   kFF—Implies a loop-carried dependency from an iteration for        which the predicate is false, to the subsequent iteration for        which the value of the predicate is false.    -   kFT—Implies a loop-carried dependency from an iteration for        which the predicate is false, to the subsequent iteration for        which the value of the predicate is true.    -   kTT—Implies a loop-carried dependency from an iteration for        which the predicate is true, to the subsequent iteration for        which the value of the predicate is true.

The 1-based (i.e., considering the vector as starting with element “1”)element position corresponding to the iteration that generates the datathat is depended upon is stored in the destination vector at the elementposition corresponding to the iteration that depends on the data. If nodata dependency exists, a value of 0 is stored in the destination vectorat that element.

Vector ConditionalStop(Vector &p, int mode) {   Vector r = 0;   for (intx=1; x<VECLEN; ++x) // Skip the first   element   {     if (p.v[x−1] ==0 && p.v[x] == 0)     {       if (mode & kFF)         r.v[x] = x;     }    else if (p.v[x−1] == 0 && p.v[x] == 1)     {       if (mode & kFT)        r.v[x] = x;     }     else if (p.v[x−1] == 1 && p.v[x] == 0)    {       if (mode & kTF)         r.v[x] = x;     }     else     {      if (mode & kTT)         r.v[x] = x;     }   }   return(r); }

Examples

~p0; a = ConditionalStop(b, kTF|kFT); On Entry: p0 = { 0 0 1 1 1 1 0 0 }a = { 9 9 9 9 9 9 9 9 } b = { 0 1 0 1 1 0 1 0 } On Exit: a = { 9 9 0 3 05 9 9 } !p0; a = ConditionalStop(b, kTF|kFT); On Entry: p0 = { 0 0 1 1 11 0 0 } a = { 9 9 9 9 9 9 9 9 } b = { 0 1 0 1 1 0 1 0 } On Exit: a = { 00 0 3 0 5 0 0 }

Remaining

This instruction sets all elements after the last non-zero element to 1,and all other elements to zero. An input vector of all zero elementsreturns all ones, and a vector of all non-zero returns all zeroes. Asshown below, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector Remaining(Vector &p) {   Vector r = 0;   int x;   for(x=VECLEN−1; x>=0; −−x) // Locate last   active predicate in input    if (p.v[x])       break;   for (++x; x<VECLEN; ++x)     r.v[x] = 1;  return(r); }

Examples

~p0; a = Remaining(b); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 99 9 9 9 } b = { 0 1 0 1 0 0 1 0 } On Exit: a = { 9 9 0 0 1 1 9 9 } !p0;a = Remaining(b); On Entry: p0 = { 0 0 1 1 1 1 0 0 } a = { 9 9 9 9 9 9 99 } b = { 0 1 0 1 0 0 1 0 } On Exit: a = { 0 0 0 0 1 1 0 0 }

CheckHazardP

This instruction examines two vectors of memory addresses (or arrayindices), corresponding to two memory operations, for potential datadependencies through memory. The vector first holds addresses for thefirst memory operation, and the vector second holds addresses for thesecond operation. The predicate pred indicates which elements of secondare to be operated upon. This instruction checks for addresses thatoverlap between each element of first and lower-numbered elements ofsecond. In the case of multiple matches, only the highest-numberedposition is recorded. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction.

The 1-based element position corresponding to the iteration thatgenerates the data that is depended upon is stored in the destinationvector at the element position corresponding to the iteration that isdependent upon the data. If no data dependency exists, a zero is storedin the destination vector at the element position corresponding to theiteration that does not have the dependency. Variants of thisinstruction should account for overlap between various sizes of datatypes.

Vector CheckHazardP(Vector &first, Vector &second, Vector &p) {   Vectorresult = 0;   int x,y;   for (x=0; x<VECLEN; ++x)     for (y=0; y<x;++y)       if (p.v[y])         if (OVERLAP(first.v[x], second.v[y]))          result.v[x] = y + 1;   return(result); }

Examples

~p0; a = CheckHazardP(b,c,p1); On Entry: p0 = { 1 1 1 1 1 1 0 0 } a = {9 9 9 9 9 9 9 9 } b = { 1 2 1 2 2 1 5 6 } c = { 1 1 2 2 4 5 6 7 } p1 = {1 1 0 1 1 1 1 1 } On Exit: a = { 0 0 2 0 4 2 9 9 } !p0; a =CheckHazardP(b,c,p1); On Entry: p0 = { 1 1 1 1 1 1 0 0 } a = { 9 9 9 9 99 9 9 } b = { 1 2 1 2 2 1 5 6 } c = { 1 1 2 2 4 5 6 7 } p1 = { 1 1 0 1 11 1 1 } On Exit: a = { 0 0 2 0 4 2 0 0 }

CheckHazardPx

This instruction examines two vectors of memory addresses (or arrayindices), corresponding to two memory operations, for potential datadependencies through memory. The vector first holds addresses for thefirst memory operation, and vector second holds addresses for the secondoperation. The predicate pred indicates which elements of second are tobe operated upon. This instruction checks for addresses that overlapbetween each element of first, and lesser-or-equal-numbered elements ofsecond. In the case of multiple matches, only the highest-numberedposition is recorded. In the case of a match against an equal-numberedelement position, the dependency is recorded in the next-highestposition in the result. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction.

The 1-based element position corresponding to the iteration thatgenerates the data that is depended upon is stored in the destinationvector at the element position corresponding to the iteration that isdependent upon the data. If no data dependency exists, a zero is storedin the destination vector at the element position corresponding to theiteration that does not have the dependency. Variants of thisinstruction should account for overlap between various sizes of datatypes.

Vector CheckHazardPx(Vector &first, Vector &second, Vector &p) {  Vector result = 0;   int x,y;   for (x=1; x<VECLEN; ++x)   {     for(y=0; y<x; ++y)       if (p.v[y])         if (OVERLAP(r.v[x], w.v[y]))          result.v[x] = y + 1;       if ((OVERLAP(r.v[x−1], w.v[x−1]) &&      p.v[x−1]))         result.v[x] = x;   }   return(result); }

Examples

~p0; a = CheckHazardPx(b,c,p1); On Entry: p0 = { 1 1 1 1 1 1 1 0 } a ={ 9 9 9 9 9 9 9 9 } b = { 1 1 2 2 5 6 3 6 } c = { 1 2 2 3 4 5 6 6 } p1 ={ 0 1 1 1 1 1 1 1 } On Exit: a = { 0 0 2 3 0 0 4 9 } !p0; a =CheckHazardPx(b,c,p1); On Entry: p0 = { 1 1 1 1 1 1 1 0 } a ={ 9 9 9 9 9 9 9 9 } b = { 1 1 2 2 5 6 3 6 } c = { 1 2 2 3 4 5 6 6 } p1 ={ 0 1 1 1 1 1 1 1 } On Exit: a = { 0 0 2 3 0 0 4 0 }

GeneratePredicates

This instruction takes a dependency index vector, DIV, and generatespredicates corresponding to the next group of elements that may safelybe processed in parallel, given the previous group that was processedwhich is indicated by prev. If no elements of prev are active,predicates are generated for the first group of elements that may safelybe processed in parallel. If prev indicates that the final elements ofthe vector have been processed, then a result vector of inactivepredicates is returned. The general pattern to determine which elementsmay safely be processed in parallel is shown below, where STARTPOS isthe first element to be considered. On exit, elements over the interval[STARTPOS, x) may safely be processed in parallel (up to but notincluding position x).

List = <empty>; for (x=STARTPOS; x<VECLEN; ++x)   if (DIV[x] in List)    Break from loop;   else if (DIV[x] > 0)     Append element position<x> to List;

The definition of GeneratePredicates follows. As shown below, in someembodiments, the instruction processes all elements equivalently;however, predication is performed by the assignment of the result, andshould be considered an integral part of this instruction.

Vector GeneratePredicates(Vector &prev, Vector &DIV) {   Vector r = 0;  int list[MAXVEC]; // 1-based list of element   positions to process inparallel   int x, y, len, done;   for (x=VECLEN−1; x>=0; −−x) // Locatelast   element processed     if (prev.v[x] )       break;   len = done =0;   for (++x; x<VECLEN; ++x)   {     for (y=0; y<len; ++y) // Compareelement of     DIV to the list of parallel elements     if (list[y] ==DIV.v[x])       done = 1;     if (!done)     {       r.v[x] = 1; //Activate the element       that is safe       list[len++] = x + 1; //Append to 1-       based list, since DIV is 1-based     }     else      break;   }   return(r); }

Examples

~p0; p1 = GeneratePredicates(p1,ix); On Entry: p0 = { 1 1 1 1 1 1 1 0 }p1 = { 0 0 0 0 0 0 0 0 } ix = { 0 0 0 2 1 3 4 0 } On Exit1: p1 ={ 1 1 1 0 0 0 0 0 } On Entry: p1 = { 1 1 1 0 0 0 0 0 } On Exit2: p1 ={ 0 0 0 1 1 1 0 0 } On Entry: p1 = { 0 0 0 1 1 1 0 0 } On Exit3: p1 ={ 0 0 0 0 0 0 1 0 } !p0; p1 = GeneratePredicates(p1,ix); On Entry: p0 ={ 1 1 1 1 1 1 1 0 } p1 = { 0 0 0 0 0 0 0 0 } ix = { 0 0 0 2 1 3 4 0 } OnExit1: p1 = { 1 1 1 0 0 0 0 0 } On Entry: p1 = { 1 1 1 0 0 0 0 0 } OnExit2: p1 = { 0 0 0 1 1 1 0 0 } On Entry: p1 = { 0 0 0 1 1 1 0 0 } OnExit3: p1 = { 0 0 0 0 0 0 1 0 }

PropagatePostT

This instruction propagates the value of active elements in s, asdetermined by predicate p, to subsequent inactive elements in thedestination. Active elements remain unchanged, and any inactive elementsthat precede the first active element also remain unchanged. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector PropagatePostT(Vector &d, Vector &s, Vector &p) {   Vector r = 0;  int x,y;   for (x=0; x<VECLEN; ++x) // Until first active   predicate,preserve dest   {     y = x;     r.v[x] = d.v[x];     if (p.v[x])      break;   }   for (++x; x<VECLEN; ++x)     if (p.v[x])     {      r.v[x] = d.v[x]; // While predicate ==       1, preserve dest      y = x;     }     else       r.v[x] = s.v[y]; // While predicate ==      0, copy final from source   return(r); }

Examples

~p0; a = PropagatePostT(a,b,p1); On Entry: p0 = { 0 1 1 1 1 1 1 1 } a ={ 8 9 A B C D E F } b = { 0 1 2 3 4 5 6 7 } p1 = { 0 0 1 1 0 0 1 0 } OnExit: a = { 8 9 A B 3 3 E 6 } !p0; a = PropagatePostT(a,b,p1); On Entry:p0 = { 0 1 1 1 1 1 1 1 } a = { 8 9 A B C D E F } b = { 0 1 2 3 4 5 6 7 }p1 = { 0 0 1 1 0 0 1 0 } On Exit: a = { 0 9 A B 3 3 E 6 }

PropagatePriorF

This instruction propagates the value of the inactive elements of src,as determined by predicate p, into subsequent active elements. Inactiveelements are copied from src to the destination. If the first element ofpredicate is active, then the last element of src is propagated to thatposition. As shown below, in some embodiments, the instruction processesall elements equivalently; however, predication is performed by theassignment of the result, and should be considered an integral part ofthis instruction.

Vector PropagatePriorF(Vector &src, Vector &p) {   Vector r = 0;   intx,y;   y = VECLEN − 1; // y is position of element to   propagate   for(x=0; x<VECLEN; ++x) // Destination unchanged   until first activepredicate   {     if (gPred.v[x] && p.v[x])       break;     r.v[x] =src.v[x];     y = x;   }   for (; x<VECLEN; ++x)   {     if (gPred.v[x]&& p.v[x])     {       r.v[x] = src.v[y]; // Propagate on       activepredicates     }     else     {       r.v[x] = src.v[x]; // Copy oninactive       predicates       y = x;     }   }   return(r); }

Examples

~p0; a = PropagatePriorF(b,p1); On Entry: p0 = { 0 1 1 1 1 1 1 0 } a ={ 9 9 9 9 9 9 9 9 } b = { 1 2 3 4 5 6 7 8 } p1 = { 0 0 1 1 0 0 1 0 } OnExit: a = { 9 2 2 2 5 6 6 9 } On Entry: p0 = { 1 1 1 1 1 1 1 0 } a ={ 9 9 9 9 9 9 9 9 } b = { 1 2 3 4 5 6 7 8 } p1 = { 1 1 0 1 0 0 1 1 } OnExit: a = { 8 8 3 3 5 6 6 9 } !p0; a = PropagatePriorF(b,p1); On Entry:p0 = { 0 1 1 1 1 1 1 0 } a = { 9 9 9 9 9 9 9 9 } b = { 1 2 3 4 5 6 7 8 }p1 = { 0 0 1 1 0 0 1 0 } On Exit: a = { 0 2 2 2 5 6 6 0 } On Entry: p0 ={ 1 1 1 1 1 1 1 0 } a = { 9 9 9 9 9 9 9 9 } b = { 1 2 3 4 5 6 7 8 } p1 ={ 1 1 1 1 0 0 1 1 } On Exit: a = { 8 8 3 3 5 6 6 0 }

CopyPropagate

This instruction copies active elements, as determined by p, from src tothe destination register. Active elements are propagated into inactiveelements in the process. Any initial inactive elements remain unchangedin the destination. As shown below, in some embodiments, the instructionprocesses all elements equivalently; however, predication is performedby the assignment of the result, and should be considered an integralpart of this instruction.

Vector CopyPropagate(Vector &dest, Vector &src, Vector &p) {   Vector r;  int x,y;   y = 0;   for (x=0; x<VECLEN; ++x) // Find first active  pred, dest unchanged     if (p.v[x] && gPred.v[x])       break;    else     {       r.v[x] = dest.v[x];       y = x;     }   for (;x<VECLEN; ++x) // If active: copy. If   inactive: propagate   {     if(p.v[x] && gPred.v[x])     {       r.v[x] = src.v[x];       y = x;     }    else       r.v[x] = src.v[y];   }   return(r); }

Examples

~p0; a = CopyPropagate(a,b,p1); On Entry: p0 = { 0 1 1 1 1 1 1 1 } a ={ 9 9 9 9 9 9 9 9 } b = { 1 2 3 4 5 6 7 8 } p1 = { 0 0 1 1 0 0 1 0 } OnExit: a = { 9 9 3 4 4 4 7 7 } !p0; a = CopyPropagate(a,b,p1); On Entry:p0 = { 0 1 1 1 1 1 1 1 } a = { 9 9 9 9 9 9 9 9 } b = { 1 2 3 4 5 6 7 8 }p1 = { 0 0 1 1 0 0 1 0 } On Exit: a = { 0 0 3 4 4 4 7 7 }

ShiftInRight

This instruction shift vector elements to the right, populating thefirst element with the input scalar j. The resulting vector is stored tothe destination register. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction. Note the four combinations of p0 andp1 in the examples below.

Vector ShiftInRight(int j, const Vector &v, int &p) {   Vector r = 0;  int x;   r.v[0] = j;   for (x=1; x<VECLEN; ++x)   {     if (gPred.v[x]&& p.v[x−1])       r.v[x] = v.v[x−1];     else if (gPred.v[x])      r.v[x] = r.v[x−1];     else       r.v[x] = v.v[x−1];   }  return(r); }

Examples

~p0; a = ShiftInRight(j,b,p1); On Entry: j = 9 p0 = { 1 1 1 0 1 1 0 1 }a = { A A A A A A A A } b = { 1 2 3 4 5 6 7 8 } p1 = { 1 0 1 1 1 1 0 1 }On Exit: a = { 9 1 1 A 4 5 A 6 } !p0; a = ShiftInRight(j,b,p1); OnEntry: j = 9 p0 = { 1 1 1 0 1 1 0 1 } a = { A A A A A A A A } b = { 1 23 4 5 6 7 8 } p1 = { 1 0 1 1 1 1 0 1 } On Exit: a = { 9 1 1 0 4 5 0 6 }

SelectLast

Use of the ShiftInRight instruction often requires that the shifted-outelement be captured to be shifted in during the next pass. Normally thisis accomplished using a simple scalar cast prior to the use ofShiftInRight. In cases where the shifted-out value may come from morethan one vector under control of predication, the SelectLast instructionis used to capture the last element from one of two vectors. Note thatthe final element for which p0 is set determines which element positionp1 is to be evaluated for.

int SelectLast(cont Vector &v1, const Vector &v2, const Vector &p) {  Vector r = 0;   int x;   for (x=VECLEN−1; x>=0; −−x)     if(gPred.v[x])       break;   if (x >= 0)     if (p.v[x])       r =v2.v[x];     else       r = v1.v[x];   return(r); }

Examples

~p0; a = SelectLast (a,b,p1); On Entry: j = 9 p0 = { 1 1 0 0 1 1 0 0 } a= { 1 2 3 4 5 6 7 8 } b = { 9 8 7 6 5 4 3 2 } p1 = { 0 0 1 1 1 0 0 1 }On Exit: a = 6 !p0; a = SelectLast (a,b,p1); On Entry: j = 9 p0 ={ 1 1 0 0 1 1 0 0 } a = { 1 2 3 4 5 6 7 8 } b = { 9 8 7 6 5 4 3 2 } p1 ={ 0 0 1 1 1 1 0 1 } On Exit: a = 4

RunningShift1R

Using the value of the first active element in a as a basis, thisinstruction right-shifts this basis by the cumulative number of bitsspecified by active elements in b. Predicate p determines which elementsparticipate in the accumulation of shift-counts. Inactive elements priorto the first active elements are copied into the destination. Thisinstruction stores the values prior to being shifted into thedestination register. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction.

Vector RunningShift1R(const Vector &a, const Vector &b, const Vector &p){   Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if (gPred.v[x] && p.v[x])     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = base >> s;     if (gPred.v[x] && p.v[x])      s += b.v[x];   }   return(r); }

Examples

~p0; a = RunningShift1R(a,b,p1); On Entry: p0 = {  1  1  1  1  1  1  1 0 } a = { 63 63 63 63 63 63 63 63 } b = {  0  1  1  2  2  3  3  4 } p1= {  0  1  1  0  0  1  1  0 } On Exit: a = { 64 64 32 16 16 16  2 64 }!p0; a = RunningShift1R(a,b,p1); On Entry: p0 = {  1  1  1  1  1  1  1 0 } a = { 63 63 63 63 63 63 63 63 } b = {  0  1  1  2  2  3  3  4 } p1= {  0  1  1  0  0  1  1  0 } On Exit: a = { 64 64 32 16 16 16  2  0 }

RunningShift2R

Using the value of the first active element in a as a basis, thisinstruction right-shifts this basis by the cumulative number of bitsspecified by active elements in b. Predicate p determines which elementsparticipate in the accumulation of shift-counts. Inactive elements priorto the first active elements are copied into the destination. Thisinstruction stores the values after being shifted into the destinationregister. As shown below, in some embodiments, the instruction processesall elements equivalently; however, predication is performed by theassignment of the result, and should be considered an integral part ofthis instruction.

Vector RunningShift2R(const Vector &a, const Vector &b, const Vector &p){   Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if (gPred.v[x] && p.v[x])     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])       s += b.v[x];    r.v[x] = base >> s;   }   return(r); }

Examples

~p0; a = RunningShift2R(a,b,p1); On Entry: p0 = {  1  1  1  1  1  1  1 0 } a = { 64 64 64 64 64 64 64 64 } b = {  0  1  1  2  2  3  3  4 } p1= {  0  1  1  0  0  1  1  0 } On Exit: a = { 64 32 16 16 16  2  0 64 }!p0; a = RunningShift2R(a,b,p1); On Entry: p0 = {  1  1  1  1  1  1  1 0 } a = { 64 64 64 64 64 64 64 64 } b = {  0  1  1  2  2  3  3  4 } p1= {  0  1  1  0  0  1  1  0 } On Exit: a = { 64 32 16 16 16  2  0  0 }

RunningShift1L

Using the value of the first active element in a as a basis, thisinstruction left-shifts this basis by the cumulative number of bitsspecified by active elements in b. Predicate p determines which elementsparticipate in the accumulation of shift-counts. Inactive elements priorto the first active elements are copied into the destination. Thisinstruction stores the values prior to being shifted into thedestination register. As shown below, in some embodiments, theinstruction processes all elements equivalently; however, predication isperformed by the assignment of the result, and should be considered anintegral part of this instruction.

Vector RunningShift1L(const Vector &a, const Vector &b, const Vector &p){   Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if (gPred.v[x] && p.v[x])     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = base << s;     if (gPred.v[x] && p.v[x])      s += b.v[x];   }   return(r); }

Examples

~p0; a = RunningShift1L(a,b,p1); On Entry: p0 = { 1 1 1 1 1 1  1 0 } a ={ 0 1 2 3 4 5  6 7 } b = { 0 1 1 1 2 2  2 3 } p1 = { 0 1 1 0 0 1  1 0 }On Exit: a = { 0 1 2 4 4 4 16 7 } !p0; a = RunningShift1L(a,b,p1); OnEntry: p0 = { 1 1 1 1 1 1  1 0 } a = { 0 1 2 3 4 5  6 7 } b = { 0 1 1 12 2  2 3 } p1 = { 0 1 1 0 0 1  1 0 } On Exit: a = { 0 1 2 4 4 4 16 0 }

RunningShift2L

Using the value of the first active element in a as a basis, thisinstruction left-shifts this basis by the cumulative number of bitsspecified by active elements in b. Predicate p determines which elementsparticipate in the accumulation of shift-counts. Inactive elements priorto the first active elements are copied into the destination. Thisinstruction stores the values after being shifted into the destinationregister. As shown below, in some embodiments, the instruction processesall elements equivalently; however, predication is performed by theassignment of the result, and should be considered an integral part ofthis instruction.

Vector RunningShift2L(const Vector &a, const Vector &b, const Vector &p){   Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if (gPred.v[x] && p.v[x])     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])       s += b.v[x];    r.v[x] = base << s;   }   return(r); }

Examples

~p0; a = RunningShift2L(a,b,p1); On Entry: p0 = { 1 1 1 1 1  1  1 0 } a= { 0 1 2 3 4  5  6 7 } b = { 0 1 1 1 2  2  2 3 } p1 = { 0 1 1 0 0  1  10 } On Exit: a = { 0 2 4 4 4 16 64 7 } !p0; a = RunningShift2L(a,b,p1);On Entry: p0 = { 1 1 1 1 1  1  1 0 } a = { 0 1 2 3 4  5  6 7 } b = { 0 11 1 2  2  2 3 } p1 = { 0 1 1 0 0  1  1 0 } On Exit: a = { 0 2 4 4 4 1664 0 }

RunningSum1P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues prior to being added into the destination register. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningSum1P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = base + s;     if ((gPred.v[x]) &&(p.v[x] ))       s += b.v[x];   }   return (r); }

Examples

~p0; a = RunningSum1P(a,b,p1); On Entry: p0 = { 1 1 1 1 1 0 1 0 } a = {0 1 2 3 4 5 6 7 } b = { 1 2 3 4 5 6 7 8 } p1 = { 1 0 1 0 1 1 1 0 } OnExit: a = { 0 1 1 4 4 5 9 7 } !p0; a = RunningSum1P(a,b,p1); On Entry:p0 = { 1 1 1 1 1 0 1 0 } a = { 0 1 2 3 4 5 6 7 } b = { 1 2 3 4 5 6 7 8 }p1 = { 1 0 1 0 1 1 1 0 } On Exit: a = { 0 1 1 4 4 0 9 0 }

RunningSum2P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues after being added into the destination register. As shown below,in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningSum2P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     if ((gPred.v[x]) && (p.v[x] ))       s += b.v[x];    r.v[x] = base + s;   }   return(r); }

Examples

~p0; a = RunningSum2P(a,b,p1); On Entry: p0 = { 1 1 1 1 1 0  1 0 } a = {0 1 2 3 4 5  6 7 } b = { 1 2 3 4 5 6  7 8 } p1 = { 1 0 1 0 1 1  1 0 } OnExit: a = { 1 1 4 4 9 5 16 7 } !p0; a = RunningSum2P(a,b,p1); On Entry:p0 = { 1 1 1 1 1 0  1 0 } a = { 0 1 2 3 4 5  6 7 } b = { 1 2 3 4 5 6  78 } p1 = { 1 0 1 0 1 1  1 0 } On Exit: a = { 1 1 4 4 9 0 16 0 }

RunningAnd1P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues prior to being added into the destination register. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningAnd1P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = −1;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = base & s;     if ((gPred.v[x]) &&(p.v[x] ))       s &= b.v[x];   }   return(r); }

Examples

~p0; a = RunningAnd1P(a,b,p1); On Entry: p0 = { 1  1  1 1 1 0 1 0 } a ={ 7 15  0 1 2 3 4 5 } b = { 7 15  7 1 6 1 4 7 } p1 = { 0  1  1 0 1 1 1 0} On Exit: a = { 7 15 15 7 7 3 6 5 } !p0; a = RunningAnd1P(a,b,p1); OnEntry: p0 = { 1  1  1 1 1 0 1 0 } a = { 7 15  0 1 2 3 4 5 } b = { 7 15 7 1 6 1 4 7 } p1 = { 0  1  1 0 1 1 1 0 } On Exit: a = { 7 15 15 7 7 0 60 }

RunningAnd2P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues after being added into the destination register. As shown below,in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningAnd2P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = −1;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     if ((gPred.v[x]) && (p.v[x] ))       s &= b.v[x];    r.v[x] = base & s;   }   return(r); }

Examples

~p0; a = RunningAnd2P(a,b,p1); On Entry: p0 = { 1  1 1 1 1 0 1 0 } a = {7 15 0 1 2 3 4 5 } b = { 7 15 7 1 6 1 4 7 } p1 = { 0  1 1 0 1 1 1 0 } OnExit: a = { 7 15 7 7 6 3 4 5 } !p0; a = RunningAnd2P(a,b,p1); On Entry:p0 = { 1  1 1 1 1 0 1 0 } a = { 7 15 0 1 2 3 4 5 } b = { 7 15 7 1 6 1 47 } p1 = { 0  1 1 0 1 1 1 0 } On Exit: a = { 7 15 7 7 6 0 4 0 }

RunningOr1P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues prior to being added into the destination register. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningOr1P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = base | s;     if ((gPred.v[x]) &&(p.v[x] ))       s |= b.v[x];   }   return(r); }

Examples

~p0; a = RunningOr1P(a,b,p1); On Entry: p0 = { 1 1 1 1 1 0 1 0 } a = { 71 9 9 9 9 9 9 } b = { 7 2 1 2 3 4 5 6 } p1 = { 0 1 1 0 1 1 1 0 } OnExit: a = { 7 1 3 3 3 9 3 9 } !p0; a = RunningOr1P(a,b,p1); On Entry: p0= { 1 1 1 1 1 0 1 0 } a = { 7 1 9 9 9 9 9 9 } b = { 7 2 1 2 3 4 5 6 } p1= { 0 1 1 0 1 1 1 0 } On Exit: a = { 7 1 3 3 3 0 3 0 }

RunningOr2P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues after being added into the destination register. As shown below,in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningOr2P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     if ((gPred.v[x]) && (p.v[x] ))       s |= b.v[x];    r.v[x] = base | s;   }   return(r); }

Examples

~p0; a = RunningOr2P(a,b,p1); On Entry: p0 = { 1 1 1 1 1 0 1 0 } a = { 71 9 9 9 9 9 9 } b = { 7 2 1 2 3 4 5 6 } p1 = { 0 1 1 0 1 1 1 0 } OnExit: a = { 7 3 3 3 3 9 7 9 } !p0; a = RunningOr2P(a,b,p1); On Entry: p0= { 1 1 1 1 1 0 1 0 } a = { 7 1 9 9 9 9 9 9 } b = { 7 2 1 2 3 4 5 6 } p1= { 0 1 1 0 1 1 1 0 } On Exit: a = { 7 3 3 3 3 0 7 0 }

RunningXor1P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues prior to being added into the destination register. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningXor1P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = base {circumflex over ( )} s;     if((gPred.v[x]) && (p.v[x] ))       s {circumflex over ( )}= b.v[x];   }  return(r); }

Examples

~p0; a = RunningXor1P(a,b,p1); On Entry: p0 = { 1 1 1 1 1 0 1 0 } a = {7 6 5 4 3 2 1 0 } b = { 7 2 1 2 3 4 5 6 } p1 = { 0 1 1 0 1 1 1 0 } OnExit: a = { 7 6 4 5 5 2 6 0 } !p0; a = RunningXor1P(a,b,p1); On Entry:p0 = { 1 1 1 1 1 0 1 0 } a = { 7 6 5 4 3 2 1 0 } b = { 7 2 1 2 3 4 5 6 }p1 = { 0 1 1 0 1 1 1 0 } On Exit: a = { 7 6 4 5 5 0 6 0 }

RunningXor2P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues after being added into the destination register. As shown below,in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningXor2P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 0;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     if ((gPred.v[x]) && (p.v[x] ))       s{circumflex over ( )}= b.v[x];     r.v[x] = base {circumflex over ( )}s;   }   return(r); }

Examples

~p0; a = RunningXor2P(a,b,p1); On Entry: p0 = { 1 1 1 1 1 0 1 0 } a = {7 6 5 4 3 2 1 0 } b = { 7 2 1 2 3 4 5 6 } p1 = { 0 1 1 0 1 1 1 0 } OnExit: a = { 7 4 5 5 6 2 3 0 } !p0; a = RunningXor2P(a,b,p1); On Entry:p0 = { 1 1 1 1 1 0 1 0 } a = { 7 6 5 4 3 2 1 0 } b = { 7 2 1 2 3 4 5 6 }p1 = { 0 1 1 0 1 1 1 0 } On Exit: a = { 7 4 5 5 6 2 3 0 }

RunningMul1P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues prior to being added into the destination register. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningMul1P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 1;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = base * s;     if ((gPred.v[x]) &&(p.v[x] ))       s *= b.v[x];   }   return(r); }

Examples

~p0; a = RunningMul1P(a,b,p1); On Entry: p0 = { 1 1 1  1  1  0  1 0 } a= { 7 6 5  4  3  2  1 0 } b = { 7 2 1  2  3  4  2 6 } p1 = { 0 1 1  0 1  1  1 0 } On Exit: a = { 7 6 12 12 12 2 36 0 } !p0; a =RunningMul1P(a,b,p1); On Entry: p0 = { 1 1 1  1  1  0  1 0 } a = { 7 65  4  3  2  1 0 } b = { 7 2 1  2  3  4  2 6 } p1 = { 0 1 1  0  1  1  1 0} On Exit: a = { 7 6 12 12 12 0 36 0 }

RunningMul2P

Using the value of the first active element in a as a basis, thisinstruction adds the cumulative amounts specified by active elements inb to this basis. Predicate p determines which elements participate inthe accumulation of addends. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues after being added into the destination register. As shown below,in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector RunningMul2P(const Vector &a, const Vector &b, const Vector &p) {  Vector r;   int s,base,x;   base = s = 1;   for (x=0; x<VECLEN; ++x)  {     if ((gPred.v[x]) && (p.v[x] ))     {       base = a.v[x];      break;     }     else       r.v[x] = a.v[x];   }   for (;x<VECLEN; ++x)   {     if ((gPred.v[x]) && (p.v[x] ))       s *= b.v[x];    r.v[x] = base * s;   }   return(r); }

Examples

~p0; a = RunningMul2P(a,b,p1); On Entry: p0 = { 1  1  1  1  1 0  1 0 } a= { 7  6  5  4  3 2  1 0 } b = { 7  2  1  2  3 4  2 6 } p1 = { 0  1  1 0  1 1  1 0 } On Exit: a = { 7 12 12 12 36 2 72 0 } !p0; a =RunningMul2P(a,b,p1); On Entry: p0 = { 1  1  1  1  1 0  1 0 } a = { 7  6 5  4  3 2  1 0 } b = { 7  2  1  2  3 4  2 6 } p1 = { 0  1  1  0  1 1  10 } On Exit: a = { 7 12 12 12 36 0 72 0 }

IncrPropagate1

Using the value of the first active element in s as a basis, thisinstruction cumulatively increments this basis for every active elementspecified by predicate p. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues prior to being incremented into the destination register. Asshown below, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector IncrPropagate1(Vector &s, Vector &p) {   Vector r;   int x,v;  for (x=0; x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])     {      v = s.v[x];       break;     }     r.v[x] = s.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = v;     if (gPred.v[x] && p.v[x])      ++v;   }   return(r); }

Examples

~p0; a = IncrPropagate1(b,p1); On Entry: p0 = { 1 1 1 1 1 1 1 0 } a = {9 9 9 9 9 9 9 9 } b = { 3 3 3 3 3 3 3 3 } p1 = { 0 1 1 0 0 1 1 0 } OnExit: a = { 3 3 4 5 5 5 6 9 } !p0; a = IncrPropagate1(b,p1); On Entry:p0 = { 1 1 1 1 1 1 1 0 } a = { 9 9 9 9 9 9 9 9 } b = { 3 3 3 3 3 3 3 3 }p1 = { 0 1 1 0 0 1 1 0 } On Exit: a = { 3 3 4 5 5 5 6 0 }

IncrPropagate2

Using the value of the first active element in s as a basis, thisinstruction cumulatively increments this basis for every active elementspecified by predicate p. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues after being incremented into the destination register. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector IncrPropagate2(Vector &s, Vector &p) {   Vector r;   int x,v;  for (x=0; x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])     {      v = s.v[x];       break;     }     r.v[x] = s.v[x];   }   for (;x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])       ++v;     r.v[x] =v;   }   return(r); }

Examples

~p0; a = IncrPropagate2(b,p1); On Entry: p0 = { 1 1 1 1 1 1 1 0 } a = {9 9 9 9 9 9 9 9 } b = { 3 3 3 3 3 3 3 3 } p1 = { 0 1 1 0 0 1 1 0 } OnExit: a = { 3 4 5 5 5 6 7 9 } !p0; a = IncrPropagate2(b,p1); On Entry:p0 = { 1 1 1 1 1 1 1 0 } a = { 9 9 9 9 9 9 9 9 } b = { 3 3 3 3 3 3 3 3 }p1 = { 0 1 1 0 0 1 1 0 } On Exit: a = { 3 4 5 5 5 6 7 0 }

DecrPropagate1

Using the value of the first active element in s as a basis, thisinstruction cumulatively decrements this basis for every active elementspecified by predicate p. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues prior to being decremented into the destination register. Asshown below, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector DecrPropagate1(Vector &s, Vector &p) {   Vector r;   int x,v;  for (x=0; x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])     {      v = s.v[x];       break;     }     r.v[x] = s.v[x];   }   for (;x<VECLEN; ++x)   {     r.v[x] = v;     if (gPred.v[x] && p.v[x])      −−v;   }   return(r); }

Examples

~p0; a = DecrPropagate1(b,p1); On Entry: p0 = { 1 1 1 1 1 1 1 0 } a = {9 9 9 9 9 9 9 9 } b = { 7 7 7 7 7 7 7 7 } p1 = { 0 1 1 0 0 1 1 0 } OnExit : a = { 7 7 6 5 5 5 4 9 } !p0; a = DecrPropagate1(b,p1); On Entry:p0 = { 1 1 1 1 1 1 1 0 } a = { 9 9 9 9 9 9 9 9 } b = { 7 7 7 7 7 7 7 7 }p1 = { 0 1 1 0 0 1 1 0 } On Exit: a = { 7 7 6 5 5 5 4 0 }

DecrPropagate2

Using the value of the first active element in s as a basis, thisinstruction cumulatively decrements this basis for every active elementspecified by predicate p. Inactive elements prior to the first activeelements are copied into the destination. This instruction stores thevalues after being decremented into the destination register. As shownbelow, in some embodiments, the instruction processes all elementsequivalently; however, predication is performed by the assignment of theresult, and should be considered an integral part of this instruction.

Vector DecrPropagate2(Vector &s, Vector &p) {   Vector r;   int x,v;  for (x=0; x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])     {      v = s.v[x];       break;     }     r.v[x] = s.v[x];   }   for (;x<VECLEN; ++x)   {     if (gPred.v[x] && p.v[x])       −−v;     r.v[x] =v;   }   return(r); }

Examples

~p0; a = DecrPropagate2(b,p1); On Entry: p0 = { 1 1 1 1 1 1 1 0 } a = {9 9 9 9 9 9 9 9 } b = { 7 7 7 7 7 7 7 7 } p1 = { 0 1 1 0 0 1 1 0 } OnExit: a = { 7 6 5 5 5 4 3 9 } !p0; a = DecrPropagate2(b,p1); On Entry:p0 = { 1 1 1 1 1 1 1 0 } a = { 9 9 9 9 9 9 9 9 } b = { 7 7 7 7 7 7 7 7 }p1 = { 0 1 1 0 0 1 1 0 } On Exit: a = { 7 6 5 5 5 4 3 0 }

Vector Read/Write Operations

The following section describes instructions for performing memoryoperations in accordance with the described embodiments. For clarity,these example instructions are described using a single data type and asingle addressing mode. Thus, the described instructions provide a basicunderstanding of the read/write operations used in the describedembodiments. In some embodiments, these instructions are extended tosupport different data types, addressing modes, etc. Some embodimentsprovide support for a virtual memory system in these instructions.

VectorReadInt

This instruction reads a vector of data from a vector of addressescalculated from the instruction's inputs. A vector of offsets in offsetare scaled by 1, 2, 4, or 8, and added to the scalar address specifiedin ptr. Data is returned in the destination register, and eithersign-extended or zero-padded depending on whether signed or unsigneddata is loaded. If this instruction is predicated, only addressescorresponding to active elements are read. The syntax for thisinstruction is:

Vector VectorReadInt(void*ptr, Vector offset);

This instruction also sets an array of indicator flags in the followingmanner: beginning at the first element, flags are set corresponding toeach element that was either successfully read, or that was not read dueto being inactive. If any element was unable to be read due toconditions that normally result in program-terminating behavior, theflag corresponding to that element is cleared (e.g., set to zero), asare all subsequent flags regardless of the status of the read operationfor those elements.

In the described embodiments, these indicator flags can be moved into avector register masked by a vector predicate. This allows loops thathave speculatively read illegal or protected memory locations to adjustthe vector elements being processed to avoid the non-existent resultsfrom the illegal read, while still faulting-in pages from the VM system.In these embodiments, the OS kernel is capable of setting these flagsbased on the VM status. In some embodiments, if the first active elementis unable to be read due to an illegal address, then a programterminating exception is generated, as forward progress cannot beguaranteed. Multiple element faults should occur as a single fault tothe OS kernel, to maximize efficiency. In the case of a memory fault ofany type, a mechanism should be provided to inform the OS kernel whichelements or addresses faulted.

Illegal reads result in zeroes being stored to the destination elementsonly for those addresses that were illegal to read.

VectorReadIntNF

This instruction reads a vector of data from a vector of addressescalculated from the instruction's inputs. A vector of offsets in offsetare scaled by 1, 2, 4, or 8, and added to the scalar address specifiedin ptr. Data is returned in the destination register. If thisinstruction is predicated, only addresses corresponding to activeelements are read. The syntax for this instruction is:

Vector VectorReadIntNF(void*ptr, Vector offset);

The VectorReadIntNF instruction is similar to the VectorReadIntinstruction, with the exception that a page fault is only taken if thefirst active address is illegal or otherwise unmapped. This provides amechanism for software speculation to avoid paging-in data from massstorage when it is uncertain that the data will actually be used. If apage fault is taken for the first active element, the OS kernel candecide whether or not to fault-in additional elements, and set theindicator flags accordingly.

VectorWriteInt

This instruction writes a vector of data in values to a vector ofaddresses calculated from the instruction's inputs. A vector of offsetsin offset are scaled by 1, 2, 4, or 8, and added to the scalar addressspecified in ptr. If this instruction is predicated, only addressescorresponding to active elements are written. Write addresses should bevalid, and there is no avoidance of program termination in the event ofa write to an illegal or protected address. The syntax for thisinstruction is:

Vector VectorWriteInt(void *ptr, Vector offset, Vector values);

VectorLEA

This instruction calculates a vector of addresses from its inputs. Avector of offsets in offset are scaled by 1, 2, 4, or 8, and added tothe scalar address specified in ptr. The syntax for this instruction is:

Vector VectorLEA(void*ptr, Vector offset);

VectorIndex

This instruction populates a vector by monotonically increasing a by bfor each vector position. The given value of a is stored in the firstelement of the destination, and b is added to every position thereafter.While the example below processes all elements, predication is performedby the assignment of the result, and should be considered an integralpart of this instruction. The syntax for this instruction is:

Vector VectorIndex(int a, int b)

This instruction operates as follows:

Vector VectorIndex(int a, int b) {   Vector r = 0;   int x;   for (x=0;x<VECLEN; ++x)     r.v[x] = a + b * x;   return(r); }

Examples

~p0; a = VectorIndex(1,2); On Entry: p0 = { 0 1 1 1 1  1  1 0 } a = { 99 9 9 9  9  9 9 } On Exit: a = { 9 3 5 7 9 11 13 9 } !p0; a =VectorIndex(1,2); On Entry: p0 = { 1 1 1 1 1  1  1 0 } a = { 9 9 9 9 9 9  9 9 } On Exit: a = { 0 3 5 7 9 11 13 0 }

Executing Program Code

FIG. 20 presents a flowchart illustrating a process for executingprogram code in accordance with the described embodiments. The processstarts with processor 102 receiving an input vector (step 2000). Theinput vector can be received from a vector register in vector registerfile 300 or can be received from elsewhere, such as L1 cache 104, memory108, or another location. The input vector includes a set of N elementsthat each contain a value (e.g., a numerical (i.e., “0” or “1”) value,characters, or another value).

Processor 102 then determines a location of a key element within theinput vector (step 2002). In some embodiments, the key element is anelement in a specified location within the vector that contains a “trueindicator.” In these embodiments, the true indicator can be apredetermined numerical value, such as “1,” “6,” or “0.” Alternatively,the true indicator can be a value from a predetermined range of values,such as any non-zero number, a particular character from a set ofcharacters, or another bit pattern.

Although we describe using a true indicator, some embodiments use a“false indicator” (i.e., the logical or bitwise opposite of a trueindicator). In addition, although we describe embodiments that use onespecified location in the received vector, in alternative embodiments,the values contained in two or more locations can be used to determinethe location of a key element. For example, a predetermined pattern ofvalues within the vector can be used to determine the location of thekey element.

In some embodiments, the specified location for the key element is theleft-most element in the input vector that contains a true indicator. Inthese embodiments, the key element can be found by starting from theleft-most element of the vector (i.e., at the zeroth element in thevector) and, proceeding to the right, determining whether each elementof the vector contains a true indictor until an element is found thatcontains a true indicator (or until the N−1st element of the vector ischecked without finding a true indicator).

In alternative embodiments, the specified location for the key elementis the right-most element in the input vector that contains a trueindicator. In these embodiments, the key element can be found bystarting from the right-most element of the vector (i.e., at the N−1stelement in the vector) and, proceeding to the left, determining whethereach element of the vector contains a true indictor until an element isfound that contains a true indicator (or until the zeroth element of thevector is checked without finding a true indicator).

Note that although we describe embodiments that search for the keyelements in particular ways, in alternative embodiments other searchpatterns can be used. For example, a complete search of the vector canbe made and from that search, the location of the key element can bedetermined. Alternatively, the search can start from a last-knownlocation of a prior key element.

Processor 102 then generates a result vector in which one or moreelements are set based on the location of the key element in the inputvector (step 2004). Note that the elements can be set to any desiredvalue (i.e., “set” does not imply a “1,” a non-zero number, or any othervalue). For example, the elements can be set to 0, 256, 0xAB2h, or 1. Inalternative embodiments, the elements in the result vector can be set toother numerical values, character values (e.g., “A” or “t”), or otherbit patterns. For example, in the described embodiments, the elements inthe result vector can be set to a true indicator or a false indicatorbased on the location of the key element in the input vector. Thus,assuming that no key element is found in the vector, some embodimentscan set all of the elements of the result vector to false indicators.

In some embodiments, the key element is the left-most element in theinput vector that contains a true indicator, and generating the resultvector involves writing true indicators in the elements in the resultvector that precede the location of the key element in the input vector,and writing false indicators in the remaining elements in the resultvector. For example, assuming that the left-most active element in theinput vector, p0, was in the fourth position of the input vector, theresult vector, r, would appear as follows:

p0 = { 0 0 0 1 1 1 0 1 } r  = { 1 1 1 0 0 0 0 0 }As shown in the preceding sections (e.g., with respect to the breakinstruction), the described embodiments can use predication or zeroingto control how the elements in the result vector are set. For example,assuming the same vector values and a predicate vector, pred, that isused for zeroing:

p0 = { 0 0 0 1 1 1 0 1 } pred = { 0 1 1 1 1 1 1 0 } r = { 0 1 1 0 0 0 00 }

In alternative embodiments, the key element is the left-most element inthe input vector that contains a true indicator, and generating theresult vector involves writing true indicators in the elements in theresult vector at or that precede the location of the key element in theinput vector, and writing false indicators in the remaining elements inthe result vector. For example, assuming that the left-most activeelement in the input vector, p0, was in the fourth position of the inputvector, the result vector, r, would appear as follows:

p0 = { 0 0 0 1 1 1 0 1 } r  = { 1 1 1 1 0 0 0 0 }Note that in this example, the true indicator is a non-zero number, “1,”and the positions in the result vector are set to “1.” Note also thatthe distinction between these embodiments and the above-describedembodiments is that for these embodiments the result vector includes atrue indicator at the position of the left-most active element, whereasthe above-described embodiments have a false indicator in this positionof the result vector. As with the example above, predication and zeroingcan be used to control how the elements in the result vector are set.

In other alternative embodiments, the key element is the right-mostelement in the input vector that contains a true indicator, andgenerating the result vector involves writing true indicators in theelements in the result vector that follow the location of the keyelement in the input vector, and writing false indicators in theremaining elements in the result vector. For example, assuming that theright-most active element in the input vector, p0, was in the fifthposition of the input vector, the result vector, r, would appear asfollows:

p0 = { 1 1 1 1 1 0 0 0 } r  = { 0 0 0 0 0 1 1 1 }Note that in this example the true indicator is a “1” and “0” is thefalse indicator. As with the examples above, predication and zeroing canbe used to control how the elements in the result vector are set.

In some embodiments, processor 102 then stores the result vector in acomputer-readable storage device. For example, processor 102 can storethe result vector in a register in vector register file 300, or canwrite the result vector to a cache (e.g., L1 cache 104) or to memory(e.g., memory 108 or mass-storage device 110). In these embodiments,storing the result vector in the computer-readable storage deviceinvolves setting or clearing (unsetting) bits in the computer-readablestorage device, thereby causing a physical transformation of the stateof the bits in the computer-readable storage device.

Although we describe generating a result vector where true indicatorsare written into the described locations, in alternative embodiments, aresult vector can be generated with true or false indicators written inany combination or pattern of elements in the result vector. Forexample, in some embodiments, the key element is the right-most elementin the input vector that contains a true indicator, and generating theresult vector involves writing true indicators in elements of the resultvector at or that follow the location of the key element in the inputvector, and writing false indicators in the remaining elements in theresult vector. Moreover, in some embodiments, generating the resultvector involves writing a true indicator only at the element of theresult vector that corresponds to the position of the key element in theinput vector, and writing false indicators in the remaining elements inthe result vector, or at a particular predetermined number of positionsin the result vector at and/or that precede or follow the location ofthe key element in the input vector.

The foregoing descriptions have been presented only for purposes ofillustration and description. They are not intended to be exhaustive orto limit the described embodiments to the forms disclosed. Accordingly,many modifications and variations will be apparent to practitionersskilled in the art. Additionally, the above disclosure is not intendedto limit the described embodiments. The scope of these embodiments isdefined by the appended claims.

1. A method for setting elements in a result vector based on an inputvector, comprising: receiving the input vector, wherein the input vectorincludes a set of N elements; determining a location of a key element inthe input vector; and generating a result vector, wherein generating theresult vector involves setting one or more elements of the result vectorbased on the location of the key element in the input vector.
 2. Themethod of claim 1, wherein the key element is a left-most element in theinput vector that contains a true indicator; and wherein generating theresult vector involves writing true indicators in the elements in theresult vector that precede the location of the key element in the inputvector, and writing false indicators in the remaining elements in theresult vector.
 3. The method of claim 2, wherein the method furthercomprises receiving a predicate vector, wherein determining a locationof the key element involves determining a location of the key element ina set of elements in the input vector for which corresponding elementsin the predicate vector contain true indicators.
 4. The method of claim1, wherein the key element is a left-most element in the input vectorthat contains a true indicator; and wherein generating the result vectorinvolves writing true indicators in the set of elements in the resultvector that are at or precede the location of the key element in theinput vector, and writing false indicators in the remaining elements inthe result vector.
 5. The method of claim 4, wherein the method furthercomprises receiving a predicate vector, wherein determining a locationof the key element involves determining a location of the key element ina set of elements in the input vector for which corresponding elementsin the predicate vector contain true indicators.
 6. The method of claim1, wherein the key element is a right-most element in the input vectorthat contains a true indicator; and wherein generating the result vectorinvolves writing true indicators in the set of elements in the resultvector that follow the location of the key element in the input vector,and writing false indicators in the remaining elements in the set ofelements in the result vector.
 7. The method of claim 6, wherein themethod further comprises receiving a predicate vector, whereindetermining a location of the key element involves determining alocation of the key element in a set of elements in the input vector forwhich corresponding elements in the predicate vector contain trueindicators.
 8. The method of claim 1, wherein determining a location ofthe key element involves: starting from a left-most or a right-mostelement in the set of elements in the input vector, evaluating a valuein each element in sequence until an element is encountered thatcontains a true indicator; and terminating the determination when anelement is found that contains a true indicator.
 9. The method of claim1, wherein the method further comprises: receiving an initial resultvector and a predicate vector, wherein the elements in the predicatevector are either active or inactive; wherein generating a result vectorinvolves updating the initial value of the result vector by setting theelements of the result vector for which corresponding elements in thepredicate vector contain true indicators to indicate the location of thekey element in the input vector; wherein generating a result vectorfurther involves one of: leaving unchanged the value in each element ofthe initial result vector for which the corresponding element in thepredicate vector contains a false indicator; or writing a falseindicator in each element of the initial result vector for which thecorresponding element in the predicate vector contains a falseindicator; and returning the updated initial result vector as the resultvector.
 10. A processor for setting elements in a result vector based onan input vector, comprising: at least one execution unit configured to:determine a location of a key element in an input vector that contains Nelements; and generate a result vector, wherein generating the resultvector involves setting one or more elements of the result vector basedon the location of the key element in the input vector.
 11. Theprocessor of claim 10, wherein the key element is a left-most element inthe input vector that contains a true indicator; and wherein whengenerating the result vector, the execution unit is configured to writetrue indicators in the elements in the result vector that precede thelocation of the key element in the input vector, and write falseindicators in the remaining elements in the result vector.
 12. Theprocessor of claim 11, wherein the execution unit is further configuredto receive a predicate vector, wherein when determining a location ofthe key element, the execution unit is configured to determine alocation of the key element in a set of elements in the input vector forwhich corresponding elements in the predicate vector contain trueindicators.
 13. The processor of claim 10, wherein the key element is aleft-most element in the input vector that contains a true indicator;and wherein when generating the result vector, the execution unit isconfigured to write true indicators in the set of elements in the resultvector that are at or precede the location of the key element in theinput vector, and write false indicators in the remaining elements inthe result vector.
 14. The processor of claim 13, wherein the executionunit is further configured to receive a predicate vector, wherein whendetermining a location of the key element, the execution unit isconfigured to determine a location of the key element in a set ofelements in the input vector for which corresponding elements in thepredicate vector contain true indicators.
 15. The processor of claim 10,wherein the key element is a right-most element in the input vector thatcontains a true indicator; and wherein when generating the resultvector, the execution unit is configured to write true indicators in theset of elements in the result vector that follow the location of the keyelement in the input vector, and write false indicators in the remainingelements in the set of elements in the result vector.
 16. The processorof claim 15, wherein the execution unit is further configured to receivea predicate vector, wherein when determining a location of the keyelement, the execution unit is configured to determine a location of thekey element in a set of elements in the input vector for whichcorresponding elements in the predicate vector contain true indicators.17. The processor of claim 10, wherein when determining a location ofthe key element, the execution unit is configured to: starting from aleft-most or a right-most element in the set of elements in the inputvector, evaluate a value in each element in sequence until an element isencountered that contains a true indicator; and terminate thedetermination when an element is found that contains a true indicator.18. The processor of claim 10, wherein the execution unit is furtherconfigured to: receive an initial result vector and a predicate vector,wherein the elements in the predicate vector are either active orinactive; wherein when generating a result vector, the execution unit isconfigured to: update the initial value of the result vector by settingthe elements of the result vector for which corresponding elements inthe predicate vector contain true indicators to indicate the location ofthe key element in the input vector; leave unchanged the value in eachelement of the initial result vector for which the corresponding elementin the predicate vector contains a false indicator, or write a falseindicator in each element of the initial result vector for which thecorresponding element in the predicate vector contains a falseindicator; and return the updated initial result vector as the resultvector.
 19. A computer system for setting elements in a result vectorbased on an input vector, comprising: a processor coupled to a memorythat stores program code and data for the processor; and at least oneexecution unit in the processor configured to: determine a location of akey element in an input vector that contains N elements; and generate aresult vector, wherein generating the result vector involves setting oneor more elements of the result vector based on the location of the keyelement in the input vector.
 20. The computer system of claim 19,wherein the key element is a left-most element in the input vector thatcontains a true indicator; and wherein when generating the resultvector, the execution unit is configured to write true indicators in theelements in the result vector that precede the location of the keyelement in the input vector, and write false indicators in the remainingelements in the result vector.
 21. The computer system of claim 20,wherein the execution unit is further configured to receive a predicatevector, wherein when determining a location of the key element, theexecution unit is configured to determine a location of the key elementin a set of elements in the input vector for which correspondingelements in the predicate vector contain true indicators.
 22. Thecomputer system of claim 19, wherein the key element is a left-mostelement in the input vector that contains a true indicator; and whereinwhen generating the result vector, the execution unit is configured towrite true indicators in the set of elements in the result vector thatare at or precede the location of the key element in the input vector,and write false indicators in the remaining elements in the resultvector.
 23. The computer system of claim 22, wherein the execution unitis further configured to receive a predicate vector, wherein whendetermining a location of the key element, the execution unit isconfigured to determine a location of the key element in a set ofelements in the input vector for which corresponding elements in thepredicate vector contain true indicators.
 24. The computer system ofclaim 19, wherein the key element is a right-most element in the inputvector that contains a true indicator; and wherein when generating theresult vector, the execution unit is configured to write true indicatorsin the set of elements in the result vector that follow the location ofthe key element in the input vector, and write false indicators in theremaining elements in the set of elements in the result vector.
 25. Thecomputer system of claim 24, wherein the execution unit is furtherconfigured to receive a predicate vector, wherein when determining alocation of the key element, the execution unit is configured todetermine a location of the key element in a set of elements in theinput vector for which corresponding elements in the predicate vectorcontain true indicators.